Vegetation coverage rate prediction method and system based on multi-source data fusion

Abstract

The invention discloses a vegetation coverage prediction method and system based on multi-source data fusion, and relates to the technical field of vegetation coverage prediction, and the method comprises the following steps: obtaining historical vegetation coverage data and land utilization type data, and obtaining a matching time sequence of land utilization transformation and vegetation recovery lag; frequency band extraction is carried out on the matching time sequence based on Fourier transform, and the initial change frequency of the matching degree is obtained; performing random disturbance on the initial change frequency of the matching degree based on a preset first constraint condition to obtain a plurality of different matching degree change frequencies; obtaining vegetation coverage degradation influence data and ecological restoration potential data corresponding to different matching degree change frequencies, and constructing an evaluation model; and according to the data output by the evaluation model, the vegetation coverage prediction value of the preset first prediction model is corrected. The problem that the vegetation coverage prediction is inaccurate due to the fact that the relation fluctuation between the land utilization type conversion speed and the vegetation recovery lag degree is not considered is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of vegetation coverage prediction, and more specifically, to a vegetation coverage prediction method and system based on multi-source data fusion. Background Art

[0002] The coupling relationship between land use and vegetation cover is particularly important in urban development. By combining land use changes with vegetation cover changes, we can effectively analyze the impact of land development and urbanization on the ecological environment. Using remote sensing data and GIS technology, combined with land use types and vegetation coverage, we can establish a correlation model between land use and vegetation cover changes, providing a scientific basis for decision-making such as urban planning and ecological restoration.

[0003] For example, the prediction method of spatiotemporal changes of vegetation coverage driven by climate change announced by the invention patent with announcement number CN114254802B includes: step 1: determining the research area and data source for establishing the vegetation prediction model; step 2: taking the historical measured vegetation coverage grid data as the dependent variable, introducing different lags as independent variables, constructing a pixel-by-pixel multiple linear regression model, and screening and constructing the best multiple linear regression model by integrating the determination coefficient of the model, the five-year average analysis method, and the results of the tendency rate analysis; step 3: taking the meteorological data in the future period as input, calculating the vegetation coverage grid data in the future period; step 4: performing downscaling processing based on the vegetation coverage grid data in the future period and the historical observation vegetation coverage data; step 5: drawing the future interannual change curve. The present invention constructs the regression model in a pixel-by-pixel manner, obtains a regression model with a higher degree of fit in the meteorological data with lower spatiotemporal resolution, and predicts the response of vegetation coverage in the future period to climate change.

[0004] The above disclosed technical solutions have at least the following technical problems: Traditional vegetation cover prediction methods ignore the mutual influence between land use type transformation and vegetation recovery lag, and cannot accurately capture the complex relationship between the two in the dynamic process.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a vegetation coverage prediction method and system based on multi-source data fusion, so as to solve the problem that the traditional method does not consider the relationship fluctuation between the speed of land use type conversion and the lag degree of vegetation recovery, resulting in inaccurate vegetation coverage prediction.

[0007] To achieve the above object, the present invention provides the following technical solutions: A method for predicting vegetation coverage by fusion of multi-source data comprises the following steps: obtaining historical vegetation coverage data and land use type data, and obtaining a matching time series of land use transformation and vegetation restoration lag; extracting frequency bands of the matching time series based on Fourier transform to obtain an initial change frequency of matching degree; performing random perturbations on the initial change frequency of matching degree based on a preset first constraint condition to obtain a number of different matching degree change frequencies; obtaining vegetation coverage degradation impact data and ecological restoration potential data corresponding to different matching degree change frequencies, and constructing an evaluation model; and correcting the vegetation coverage prediction value of the preset first prediction model according to the data output by the evaluation model.

[0008] In a preferred embodiment, the method of obtaining historical vegetation cover data and land use type data, and obtaining a matching time series of land use transformation and vegetation restoration lags, is specifically as follows: obtaining historical vegetation cover data and land use type data, and constructing a land use type transfer matrix; calculating the trace of the area change of each land use type in adjacent periods based on the land use type transfer matrix, and obtaining a land use transformation rate time series; constructing a time-lagged cross-correlation function of the land use area change rate and the vegetation coverage change rate of each land use type; obtaining the time delay when the time-lagged cross-correlation function first reaches a peak value, and using the time delay as a lag period; obtaining the length of the lag period and calculating the vegetation restoration lag rate series, and using the cubic spline interpolation method to time align the land use transformation rate time series and the vegetation restoration lag rate series to obtain a matching time series.

[0009] In a preferred embodiment, the frequency band extraction of the matching degree time series based on Fourier transform to obtain the initial change frequency of the matching degree is specifically as follows: Fourier transform the matching degree time series using a sliding window mechanism, and extract the main frequency band in each window whose amplitude ratio exceeds a preset threshold; calculate the frequency mean of the main frequency band and use it as the initial change frequency.

[0010] In a preferred embodiment, the initial change frequency of the matching degree is randomly disturbed based on the preset first constraint condition to obtain several different matching degree change frequencies, specifically: the maximum vegetation recovery lag rate, land use transformation rate and average lag period are calculated, and the first constraint condition is constructed; according to the first constraint condition, the disturbance amplitude of the Poisson distribution is obtained; the Poisson distribution is used for random disturbance to obtain several random disturbance terms within the disturbance amplitude; the random disturbance terms are added to the initial change frequency of the matching degree to obtain several different matching degree change frequencies.

[0011] In a preferred embodiment, the vegetation cover degradation impact data includes vegetation cover degradation impact characteristics, and the specific method for obtaining the vegetation cover degradation impact characteristics is as follows: obtain multi-phase remote sensing NDVI data, and calculate the vegetation cover change rate of each phase; perform data analysis on the vegetation cover change rate, identify vegetation degradation hotspot areas and calculate the standard deviation and mean of the NDVI change rate in the vegetation degradation hotspot areas; obtain the degradation fluctuation value according to the standard deviation and mean of the NDVI change rate; obtain climate data, calculate the ratio of precipitation in the drought period to the annual average precipitation, and obtain the drought frequency index; calculate the vegetation cover degradation impact characteristics based on the degradation fluctuation value and the drought frequency index.

[0012] In a preferred embodiment, the ecological restoration potential data include ecological restoration potential characteristics, and the specific method for obtaining the ecological restoration potential characteristics is as follows: obtain a species distribution database, and calculate the overlap between historical vegetation coverage and species suitable areas to obtain a first potential value; obtain remote sensing precipitation data, and calculate the water collection rate corresponding to different slopes; calculate the mean and variance of the water collection rate, and use the ratio of the mean and variance of the water collection rate as the terrain wetness index; calculate the ecological restoration potential characteristics based on the first potential value and the terrain wetness index.

[0013] The technical effects and advantages of the vegetation coverage prediction method and system based on multi-source data fusion of the present invention are as follows: 1. The present invention introduces the land use transformation rate and the vegetation recovery lag rate, uses Fourier transform analysis to match the time series, extracts the frequency of change in the matching degree, and then quantifies and corrects the vegetation recovery lag. This method, based on the evaluation model, combines multi-temporal remote sensing data and climate data, not only considers the speed of land use type transformation, but also comprehensively analyzes the lag effect of vegetation recovery, effectively improving the accuracy of vegetation coverage prediction. In addition, by introducing the random perturbation technology of Poisson distribution, a variety of possible change frequencies are provided for the prediction model, which enhances the robustness and adaptability of the model, solves the problem that the traditional method does not consider the relationship fluctuation between the speed of land use type conversion and the lag degree of vegetation recovery, thereby leading to inaccurate vegetation coverage prediction, and improves the accuracy of vegetation coverage prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention is a flowchart of a method for predicting vegetation coverage by fusion of multi-source data.

[0015] Figure 2 The present invention is a structural schematic diagram of a vegetation coverage prediction system based on multi-source data fusion. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1, Figure 1 The present invention provides a vegetation coverage prediction method based on multi-source data fusion, which includes the following steps: S1, obtain historical vegetation cover data and land use type data, and obtain the matching time series of land use transition and vegetation restoration lag; In this example, historical vegetation cover data and land use type data are obtained, and the matching time series of land use transition and vegetation restoration lag are obtained, specifically: Obtain historical vegetation cover data and land use type data, and construct a land use type transfer matrix; Based on the land use type transfer matrix, the trace of the area change of each land use type in adjacent periods is calculated to obtain the land use transformation rate time series; Construct the time-lagged cross-correlation function between the land use area change rate and the vegetation coverage change rate of each land use type; Obtain the time delay when the time-delay cross-correlation function reaches a peak for the first time, and use the time delay as the lag period; The length of the lag period is obtained and the vegetation recovery lag rate series is calculated. The land use transformation rate time series and the vegetation recovery lag rate series are aligned using the cubic spline interpolation method to obtain a matching time series.

[0018] Among them, the specific calculation formula for the matching degree time series of land use transformation rate and vegetation recovery lag rate is as follows:

[0019] in, is the matching degree at the tth moment, is the trace of the change in area of ​​each land use type in adjacent periods, is the preset weight coefficient, is the vegetation coverage of the i-th land use type, is the average vegetation coverage, is the number of land use types.

[0020] It should be noted that the historical vegetation coverage data and land use type data are first obtained, and a land use type transfer matrix is ​​constructed. Through this matrix, the trace of the area change of each land use type in adjacent periods can be calculated, and then the land use transformation rate time series can be obtained. Next, based on the time-lagged cross-correlation function between the area change rate of the land use type and the vegetation coverage change rate, the time-lag relationship between the land use area change and the vegetation coverage change of each land use type is calculated, and by analyzing the peak position of the time-lagged cross-correlation function, the time-lag period, that is, the lag effect of the impact of land use transformation on vegetation restoration is obtained. Furthermore, by calculating the vegetation recovery lag rate series, the land use transformation rate time series and the vegetation recovery lag rate series are time-aligned in combination with the cubic spline interpolation method to obtain the final matching time series. The specific advantages are as follows: First, by aligning the time of land use transformation rate and vegetation restoration lag rate, the long-term impact of land use change on vegetation restoration can be comprehensively and systematically revealed. In particular, the factor of time lag is taken into account, which can effectively avoid the deviation caused by the traditional method of ignoring the time effect. Secondly, the cubic spline interpolation method is used to smooth the time series of different periods, which helps to eliminate the interference of time series fluctuations on the analysis results and improve the accuracy and reliability of the results. Finally, the analysis method based on the time-lag cross-correlation function can more accurately capture the intrinsic relationship between land use change and vegetation restoration, thereby providing a scientific basis for the formulation of land use management, ecological restoration and sustainable development strategies.

[0021] S2, extract the frequency band of the matching time series based on Fourier transform to obtain the initial change frequency of the matching degree; In this example, frequency bands are extracted from the matching degree time series based on Fourier transform to obtain the initial change frequency of the matching degree, specifically: The sliding window mechanism is used to perform Fourier transform on the matching degree time series, and the main frequency band whose amplitude ratio exceeds the preset threshold in each window is extracted; Calculate the frequency mean of the main frequency band and use it as the initial change frequency.

[0022] It should be noted that the sliding window mechanism is used to perform Fourier transform on the matching degree time series, and the main frequency band whose amplitude ratio exceeds the preset threshold is extracted in each window. Through Fourier transform, the time domain signal can be converted to the frequency domain to reveal the change characteristics of different frequency components in the time series. In each sliding window, the main frequency band whose amplitude ratio exceeds the preset threshold is selected to ensure that the extracted frequency band is closely related to the main change mode of the time series. Then, the frequency mean of the main frequency band in each window is calculated, and the frequency mean is used as the initial change frequency to further analyze the change trend of the time series. The specific advantages are as follows: Converting the matching degree time series from the time domain to the frequency domain through Fourier transform can reveal the potential periodic changes and frequency characteristics, making the analysis results more intuitive and clear; secondly, using the sliding window mechanism to perform local frequency domain analysis on the time series can capture the dynamic changes in the time series and the frequency fluctuations in different time periods, avoiding the defect that the overall analysis may ignore local changes; thirdly, by extracting the frequency bands whose amplitude ratio exceeds the preset threshold, the noise component can be effectively removed to ensure that the extracted frequency bands are closely related to the actual change frequency of the matching degree; finally, calculating the frequency mean of the main frequency band and using it as the initial change frequency helps to accurately grasp the initial change trend of the time series, providing a stable and reliable frequency estimate, and providing a solid foundation for subsequent trend analysis and prediction.

[0023] S3, randomly perturbing the initial change frequency of the matching degree based on the preset first constraint condition to obtain a plurality of different matching degree change frequencies; In this example, the initial change frequency of the matching degree is randomly perturbed based on the preset first constraint condition to obtain several different matching degree change frequencies, specifically: Calculate the maximum vegetation recovery hysteresis rate, land use transformation rate and average hysteresis period, and construct the first constraint condition; According to the first constraint condition, the disturbance amplitude of the Poisson distribution is obtained; Use Poisson distribution to perform random perturbations, and obtain several random perturbation terms within the perturbation amplitude; The random disturbance term is added to the initial change frequency of the matching degree to obtain several different matching degree change frequencies.

[0024] The specific formula of the first constraint is as follows: in, is the frequency disturbance, is the initial change frequency of the matching degree, is the maximum vegetation recovery hysteresis rate, is the land use transition rate, is the average lag period.

[0025] It should be noted that, according to the first constraint, the disturbance amplitude of the Poisson distribution is obtained. The Poisson distribution is used to simulate the randomness of the disturbance. Its characteristic is that it can reflect the fluctuations within a certain range, thereby providing a probability distribution basis for subsequent random disturbances. Next, the Poisson distribution is used to randomly perturb the initial change frequency of the matching degree to obtain random disturbance terms within several disturbance amplitudes. These random disturbance terms are added to the initial change frequency of the matching degree to generate multiple different matching degree change frequencies. The specific advantages are as follows: By calculating the maximum vegetation recovery lag rate, land use transformation rate and average lag period, a reasonable and scientific first constraint condition was constructed to ensure that the disturbance process would not deviate from the actual system change law, thus ensuring the accuracy and reliability of the model; secondly, the use of Poisson distribution for perturbation can simulate system changes under different scenarios by introducing randomness, providing a variety of frequency changes for analysis and avoiding the limitations of a single model; thirdly, by adding the disturbance term to the initial change frequency of the matching degree, multiple perturbation frequencies can be generated.

[0026] S4, obtain the vegetation cover degradation impact data and ecological restoration potential data corresponding to different matching degree change frequencies, and build an evaluation model; In this example, the vegetation cover degradation impact data includes vegetation cover degradation impact characteristics, and the specific method for obtaining the vegetation cover degradation impact characteristics is as follows: Obtain multi-temporal remote sensing NDVI data and calculate the vegetation coverage change rate of each phase; Data analysis was performed on vegetation cover change rate, vegetation degradation hotspots were identified, and the standard deviation and mean of NDVI change rate in vegetation degradation hotspots were calculated; Based on the standard deviation and mean of the NDVI change rate, the degradation fluctuation value is obtained; Obtain climate data, calculate the ratio of precipitation during the drought period to the annual average precipitation, and obtain the drought frequency index; The impact characteristics of vegetation cover degradation were calculated based on the degradation fluctuation value and drought frequency index.

[0027] Among them, the specific calculation formula for the impact characteristics of vegetation cover degradation is as follows:

[0028] in, The impact characteristics of vegetation cover degradation are: is the degradation fluctuation value, is the drought frequency index, is the vegetation cover change rate, is the preset weight coefficient.

[0029] In this example, the ecological restoration potential data includes ecological restoration potential characteristics, and the specific method for obtaining the ecological restoration potential characteristics is as follows: Obtain the species distribution database, and calculate the overlap between the historical vegetation coverage and the species' suitable habitat to obtain the first potential value; Obtain remote sensing precipitation data and calculate the water collection rate corresponding to different slopes; The mean and variance of the water collection rate were calculated, and the ratio of the mean and variance of the water collection rate was used as the terrain wetness index; The ecological restoration potential characteristics were calculated based on the first potential value and terrain wetness index.

[0030] The calculation formula of ecological restoration potential characteristics is as follows:

[0031] in, For ecological restoration potential characteristics, is the terrain wetness index, is the seed dispersal distance, is the maximum propagation distance, and are the preset weight coefficients respectively.

[0032] In this example, an evaluation model is constructed as follows: The impact characteristics of vegetation cover degradation and ecological restoration potential were input into the random forest model; The random forest model was trained using a cross-validation strategy to obtain the matching degree change frequency evaluation value.

[0033] It should be noted that the characteristics of vegetation cover degradation and ecological restoration potential are used as input variables to reflect the vegetation degradation during the land use transformation process and its corresponding ecological restoration capacity. These characteristics include but are not limited to data on land use changes, vegetation coverage, soil quality, etc., which can fully capture the dynamic change characteristics of the ecosystem. Subsequently, the random forest model is used to model these characteristics. Random forest is an integrated learning algorithm that uses a combination of multiple decision trees for predictive analysis. It has strong noise resistance and efficient feature selection capabilities.

[0034] During the model training process, a cross-validation strategy was used for optimization to ensure the robustness and generalization ability of the model. Cross-validation effectively avoids overfitting and improves the prediction accuracy of the model by dividing the data set into multiple subsets and using some of the data for training and validation in turn. The trained random forest model can output an evaluation value of the matching change frequency based on the input vegetation cover degradation impact characteristics and ecological restoration potential characteristics, reflecting the potential laws and trends of vegetation cover changes during ecological restoration. The specific advantages are as follows: First, the combined input of vegetation cover degradation and ecological restoration potential characteristics can comprehensively describe the impact of land use transition on the ecosystem, avoiding the limitations of single factor analysis; second, the random forest model can better capture the nonlinear relationship between features by integrating multiple decision trees for prediction, avoiding the linear assumption limitations in traditional regression analysis, and enhancing the accuracy and robustness of the model; third, the cross-validation strategy can effectively improve the generalization ability of the model, reduce errors caused by insufficient or biased training data, and ensure the reliability of the evaluation results; finally, this method provides a quantitative basis for the ecological restoration potential of land use transition by evaluating the frequency of changes in matching degree, which can provide scientific decision-making support for land managers and ecological restoration planners, and has important practical application value.

[0035] S5, modifying the vegetation coverage prediction value of the preset first prediction model according to the data output by the evaluation model.

[0036] In this example, the vegetation coverage prediction value of the preset first prediction model is corrected according to the data output by the evaluation model, specifically: Based on the evaluation model, the matching degree change evaluation value is obtained; In a three-dimensional coordinate system, a three-dimensional prediction surface is constructed with the matching degree change frequency, matching degree change evaluation value, and prediction coverage as coordinate axes; Calculate the feature points on the three-dimensional prediction surface whose gradient change rate exceeds a threshold value to obtain a feature point set; Cluster the feature point set to obtain several mutation regions; The correction weight is calculated according to the area of ​​the mutation region, and the predicted value of vegetation coverage is corrected according to the correction weight.

[0037] It should be noted that based on the evaluation model, the matching change evaluation value is obtained, which is obtained through dynamic analysis of the land use transformation rate and the vegetation recovery lag rate, and can fully reflect the impact of land use change on vegetation coverage. Then, in the three-dimensional coordinate system, a three-dimensional prediction surface is constructed with the matching change frequency, matching change evaluation value and predicted coverage as coordinate axes. The construction of this three-dimensional surface can effectively integrate multiple influencing factors, so that the prediction model can more accurately describe the vegetation coverage in multiple dimensions of space and time.

[0038] Furthermore, by calculating the gradient change rate on the three-dimensional prediction surface and identifying the feature points that exceed the set threshold, the mutation areas in the vegetation coverage change can be accurately captured. These mutation areas may represent important ecological changes or key land use transition areas, and are areas that require special attention in the prediction. By performing cluster analysis on these feature point sets, they can be divided into several mutation areas, thereby finding the key driving factors or change trends of vegetation coverage changes.

[0039] Finally, the correction weights are calculated based on the areas of these mutation regions, and the predicted values ​​of vegetation coverage are corrected using the weights. The correction process further adjusts and optimizes the preliminary prediction results by combining the geographical characteristics and ecological changes of the mutation regions to ensure that the predicted values ​​are closer to the actual changes. This method not only takes into account the dynamic changes in time and space, but also combines important factors such as land use transition and vegetation recovery lag, which can significantly improve the accuracy of the prediction model. The specific advantages are as follows: Through the construction of three-dimensional prediction surfaces and the analysis of gradient change rates, the mutation areas in vegetation coverage changes can be accurately identified, avoiding the limitation of traditional methods that cannot handle mutation changes; through cluster analysis and the introduction of correction weights, the predicted values ​​of vegetation coverage are further optimized, and the accuracy and adaptability of the model are enhanced. In addition, the calculation of correction weights can make differential corrections to the predicted values ​​of different regions according to the actual conditions of the ecological region, making the prediction results more accurate in spatial distribution and having stronger practical application value and operability.

[0040] Embodiment 2, Figure 2 The present invention provides a vegetation coverage prediction system based on multi-source data fusion, including a data acquisition module, a frequency analysis module, a disturbance generation module, a model construction module and a prediction correction module: Data acquisition module, used to obtain historical vegetation cover data and land use type data, and obtain matching time series of land use transition and vegetation restoration lag; The frequency analysis module is used to extract the frequency band of the matching time series based on Fourier transform to obtain the initial change frequency of the matching degree; A disturbance generation module, used for randomly perturbing the initial change frequency of the matching degree based on a preset first constraint condition to obtain a plurality of different matching degree change frequencies; The model building module is used to obtain the vegetation cover degradation impact data and ecological restoration potential data corresponding to different matching degree change frequencies, and to build an evaluation model; The prediction correction module is used to correct the vegetation coverage prediction value of the preset first prediction model according to the data output by the evaluation model.

[0041] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0042] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0043] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0044] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0045] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0046] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A vegetation coverage prediction method based on multi-source data fusion, characterized in that: The following steps are involved: Obtain historical vegetation cover data and land use type data, and obtain matching time series of land use transition and vegetation restoration lags; Based on Fourier transform, frequency bands of matching time series are extracted to obtain the initial change frequency of matching degree; Based on the preset first constraint condition, the initial change frequency of the matching degree is randomly disturbed to obtain a plurality of different matching degree change frequencies; Obtain the vegetation cover degradation impact data and ecological restoration potential data corresponding to different matching degree change frequencies, and build an evaluation model; The vegetation coverage prediction value of the preset first prediction model is corrected according to the data output by the evaluation model.

2. The vegetation coverage prediction method based on multi-source data fusion according to claim 1 is characterized in that: The acquisition of historical vegetation cover data and land use type data, and the acquisition of the matching time series of land use transition and vegetation restoration lag, are specifically: Obtain historical vegetation cover data and land use type data, and construct a land use type transfer matrix; Based on the land use type transfer matrix, the trace of the area change of each land use type in adjacent periods is calculated to obtain the land use transformation rate time series; Construct the time-lagged cross-correlation function between the land use area change rate and the vegetation coverage change rate of each land use type; Obtain the time delay when the time-delay cross-correlation function reaches a peak for the first time, and use the time delay as the lag period; The length of the lag period is obtained and the vegetation recovery lag rate series is calculated. The land use transformation rate time series and the vegetation recovery lag rate series are aligned using the cubic spline interpolation method to obtain a matching time series.

3. The vegetation coverage prediction method based on multi-source data fusion according to claim 2 is characterized in that: The frequency band extraction of the matching time series based on Fourier transform is performed to obtain the initial change frequency of the matching degree, which is specifically: The sliding window mechanism is used to perform Fourier transform on the matching degree time series, and the main frequency band whose amplitude ratio exceeds the preset threshold in each window is extracted; Calculate the frequency mean of the main frequency band and use it as the initial change frequency.

4. The vegetation coverage prediction method based on multi-source data fusion according to claim 3 is characterized in that: The initial change frequency of the matching degree is randomly disturbed based on the preset first constraint condition to obtain a plurality of different matching degree change frequencies, specifically: Calculate the maximum vegetation recovery hysteresis rate, land use transformation rate and average hysteresis period, and construct the first constraint condition; According to the first constraint condition, the disturbance amplitude of the Poisson distribution is obtained; Use Poisson distribution to perform random perturbations, and obtain several random perturbation terms within the perturbation amplitude; The random disturbance term is added to the initial change frequency of the matching degree to obtain several different matching degree change frequencies.

5. The vegetation coverage prediction method based on multi-source data fusion according to claim 4 is characterized in that: The vegetation cover degradation impact data includes vegetation cover degradation impact characteristics, and the specific method for obtaining the vegetation cover degradation impact characteristics is as follows: Obtain multi-temporal remote sensing NDVI data and calculate the vegetation coverage change rate of each phase; Data analysis was performed on vegetation cover change rate, vegetation degradation hotspots were identified, and the standard deviation and mean of NDVI change rate in vegetation degradation hotspots were calculated; Based on the standard deviation and mean of the NDVI change rate, the degradation fluctuation value is obtained; Obtain climate data, calculate the ratio of precipitation during the drought period to the annual average precipitation, and obtain the drought frequency index; The impact characteristics of vegetation cover degradation were calculated based on the degradation fluctuation value and drought frequency index.

6. The vegetation coverage prediction method based on multi-source data fusion according to claim 5 is characterized in that: The ecological restoration potential data includes ecological restoration potential characteristics, and the specific method for obtaining the ecological restoration potential characteristics is as follows: Obtain the species distribution database, and calculate the overlap between the historical vegetation coverage and the species' suitable habitat to obtain the first potential value; Obtain remote sensing precipitation data and calculate the water collection rate corresponding to different slopes; The mean and variance of the water collection rate were calculated, and the ratio of the mean and variance of the water collection rate was used as the terrain wetness index; The ecological restoration potential characteristics were calculated based on the first potential value and terrain wetness index.

7. The vegetation coverage prediction method based on multi-source data fusion according to claim 6 is characterized in that: The vegetation coverage prediction value of the preset first prediction model is corrected according to the data output by the evaluation model, specifically: Based on the evaluation model, the matching degree change evaluation value is obtained; In a three-dimensional coordinate system, a three-dimensional prediction surface is constructed with the matching degree change frequency, matching degree change evaluation value, and prediction coverage as coordinate axes; Calculate the feature points on the three-dimensional prediction surface whose gradient change rate exceeds a threshold value to obtain a feature point set; Cluster the feature point set to obtain several mutation regions; The correction weight is calculated according to the area of ​​the mutation region, and the predicted value of vegetation coverage is corrected according to the correction weight.

8. The vegetation coverage prediction method based on multi-source data fusion according to claim 7 is characterized in that: The specific formula of the first constraint condition is as follows: in, is the frequency disturbance, is the initial change frequency of the matching degree, is the maximum vegetation recovery hysteresis rate, is the land use transition rate, is the average lag period.

9. A vegetation coverage prediction system based on multi-source data fusion, applied to a vegetation coverage prediction method based on multi-source data fusion as claimed in any one of claims 1 to 8, characterized in that: It includes data acquisition module, frequency analysis module, disturbance generation module, model building module and prediction correction module: Data acquisition module, used to obtain historical vegetation cover data and land use type data, and obtain matching time series of land use transition and vegetation restoration lag; The frequency analysis module is used to extract the frequency band of the matching time series based on Fourier transform to obtain the initial change frequency of the matching degree; A disturbance generation module, used for randomly perturbing the initial change frequency of the matching degree based on a preset first constraint condition to obtain a plurality of different matching degree change frequencies; Model building module, used to obtain vegetation cover degradation impact data and ecological restoration potential data corresponding to different matching degree change frequencies, and build an evaluation model; The prediction correction module is used to correct the vegetation coverage prediction value of the preset first prediction model according to the data output by the evaluation model.

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