Ecological fragile area identification and ecological restoration method and system based on remote sensing image

Abstract

The invention relates to an ecologically vulnerable area identification and ecological restoration method and system based on a remote sensing image. The method comprises the following steps: collecting optical and synthetic aperture radar multi-source remote sensing data, calculating a space correction parameter to complete image registration, and filling a cloud layer shielding area by adopting time sequence interpolation to generate a complete remote sensing image; inputting the complete image into a soil humidity inversion model to generate humidity time sequence data, extracting vegetation parameters to determine a change trend, and delimiting an ecological degradation area through correlation analysis and trend matching of the two; extracting time-space distribution characteristics of the degradation area to judge an abnormal area, processing data of the abnormal area by adopting a spatial interpolation algorithm, and quantitatively evaluating a degradation risk level in combination with a multi-level risk standard; and verifying the ecological restoration effect based on the risk level, and comparing the initial monitoring data to generate a standardized restoration effect index. By adopting the method, the pertinence and effectiveness of ecologically vulnerable region identification and restoration can be improved, and the manual investigation cost and period can be reduced.

Description

Technical Field

[0001] This invention belongs to the field of ecological environment monitoring, and in particular relates to a method and system for identifying and restoring ecologically vulnerable areas based on remote sensing images. Background Technology

[0002] Dynamic monitoring and precise restoration of ecologically fragile areas have become one of the core needs in the field of ecological environmental protection. Remote sensing technology, with its advantages of large-scale, all-weather, and multi-temporal observation, is widely used in the identification and assessment of ecologically fragile areas. Existing technologies mostly rely on single optical remote sensing images or synthetic aperture radar images to perform ecological parameter inversion and fragile area delineation. However, single data sources are easily affected by factors such as cloud cover and terrain shadows, resulting in insufficient image data integrity and difficulty in accurately capturing the spatiotemporal evolution patterns of ecologically fragile areas. At the same time, traditional fragile area identification methods mostly rely on static threshold judgments and lack spatiotemporal coupling analysis of key ecological parameters such as soil moisture and vegetation cover, which can easily lead to blurred fragile area boundaries and low identification accuracy. In addition, existing ecological restoration effect verification mostly uses manual field surveys or single-point monitoring data, which has the disadvantages of long cycle, high cost, and limited coverage, and lacks a standardized quantitative evaluation system, making it difficult to achieve dynamic adjustment and optimization of restoration plans. Summary of the Invention

[0003] Therefore, it is necessary to provide a method and system for identifying and restoring ecologically vulnerable areas based on remote sensing images, which can improve the pertinence and effectiveness of ecological restoration work and reduce the cost and time of manual field investigation, in order to address the above-mentioned technical problems.

[0004] Firstly, this application provides a method for identifying and restoring ecologically vulnerable areas based on remote sensing imagery, including:

[0005] Multi-source remote sensing data, including optical images and synthetic aperture radar images, are collected. Correction parameters are calculated based on the multi-source remote sensing data to adjust the spatial position of the images. If there are cloud-covered areas, a temporal interpolation method is used to fill in the pixel values ​​to generate a complete remote sensing image.

[0006] The complete remote sensing image is input into the constructed soil moisture inversion model to generate moisture time series data and determine the vegetation change trend. Correlation analysis and trend matching are performed on the vegetation change trend and moisture time series data to identify ecologically degraded areas.

[0007] The spatiotemporal distribution characteristics of ecologically degraded areas are extracted, abnormal areas are identified based on the spatiotemporal distribution characteristics, and spatial interpolation algorithms are used to interpolate the abnormal areas. The degradation risk level is then assessed in conjunction with multi-level risk standards.

[0008] The restoration effect of ecological restoration areas was verified based on the degradation risk level, and restoration effect indicators were obtained by comparing and analyzing the initial monitoring data.

[0009] In one embodiment, correction parameters are calculated based on multi-source remote sensing data to adjust the spatial position of the image. If cloud cover is present, a temporal interpolation method is used to fill in pixel values ​​and generate a complete remote sensing image, including:

[0010] Image registration error data corresponding to multi-source remote sensing data is extracted, and the error distribution matrix is ​​calculated by least squares fitting algorithm on the image registration error data to generate error correction parameters.

[0011] Error correction parameters are used to perform spatial position adjustments on the original optical images and synthetic aperture radar images of multi-source remote sensing data, generating a multi-source image set with registration optimization.

[0012] The system performs radiation scale uniformity correction and imaging mechanism difference compensation processing on optical images and synthetic aperture radar images from multi-source image sets, respectively, and outputs standardized image data.

[0013] Based on standardized image data, the spectral, texture, and topographic features of multi-source images are determined. Then, time series consistency correction processing is performed on the time series images corresponding to the image features to generate a correction dataset.

[0014] The calibration dataset is preprocessed with radiometric calibration to determine whether the radiometric accuracy meets the preset accuracy threshold. If it does, the preprocessed image set is generated.

[0015] The preprocessed image set is subjected to pixel-level fusion processing based on a weighted fusion algorithm to obtain fused image data.

[0016] The cloud-occluded areas in the fused image data are detected. If they exist, a temporal interpolation method is used to fill the pixel values ​​of the occluded areas to generate a complete remote sensing image.

[0017] In one embodiment, complete remote sensing images are input into a pre-constructed soil moisture inversion model to generate moisture time-series data and determine vegetation change trends. Correlation analysis and trend matching are performed on the vegetation change trends and moisture time-series data to identify ecologically degraded areas, including:

[0018] Acquire multi-temporal image data corresponding to complete remote sensing images, extract near-infrared band reflectance data and red band reflectance data from the multi-temporal image data, perform difference and ratio calculations on the band reflectance data, and generate vegetation index data.

[0019] Based on vegetation index data, time series features are extracted, and a preset time series analysis algorithm is used to perform trend analysis on the time series features to determine the vegetation change trend.

[0020] The image data corresponding to the complete remote sensing image is obtained, and the image data is processed by inversion operation based on the preset soil moisture inversion model to generate soil moisture time series data; the soil moisture inversion model is constructed based on the random forest machine learning algorithm.

[0021] Correlation analysis and trend matching were performed on the temporal characteristics of soil moisture time series data and vegetation change trends to determine the ecological evolution status and obtain the corresponding ecological dynamic change pattern.

[0022] Determine whether the ecological dynamic change pattern shows a continuous downward trend in vegetation index and soil moisture and is below the preset ecological degradation trend threshold. If so, identify the ecological degradation area.

[0023] In one embodiment, the spatiotemporal distribution characteristics of ecologically degraded areas are extracted, abnormal areas are identified based on these characteristics, and spatial interpolation algorithms are used to interpolate the abnormal areas. The degradation risk level is then assessed using a multi-level risk standard, including:

[0024] Obtain the trend parameters of ecological degradation areas, and perform outlier removal and data normalization on the trend parameters in sequence to generate a standardized trend dataset.

[0025] A fitting algorithm is used to fit the temporal features of the trend dataset, and a clustering algorithm is used to perform spatial feature clustering analysis on the fitted dataset to obtain the spatiotemporal distribution features.

[0026] Determine whether the spatiotemporal distribution characteristics exceed the preset distribution threshold range; if so, generate corresponding alarm signal data.

[0027] Based on the threshold triggering conditions of alarm signal data, abnormal distribution features that perfectly match the alarm threshold in ecologically degraded areas are extracted to generate abnormal area identifiers.

[0028] Spatial interpolation algorithms are used to interpolate the spatiotemporal data corresponding to the abnormal area identifiers to generate spatiotemporal change patterns of ecologically degraded areas. Combined with the preset multi-level degradation risk level classification standards, the degradation risk level is quantitatively assessed.

[0029] In one embodiment, the restoration effect of the ecological restoration area is verified based on the degradation risk level. The restoration effect indicators are obtained by comparing and analyzing initial monitoring data, including:

[0030] Based on the degradation risk level, key ecological characteristic parameters associated with the corresponding ecological restoration areas are extracted to generate a feature dataset.

[0031] Temporal trend fitting and spatial heterogeneity calculation were performed on each key ecological feature parameter of the feature dataset. Combined with the preset restoration target dynamic adaptation coefficient weighted fusion, preliminary values ​​of restoration effect indicators were generated.

[0032] The preliminary values ​​of the restoration effect indicators are compared with the initial ecological monitoring data of the corresponding restoration area by performing difference calculation and deviation quantification analysis to generate restoration effect deviation data.

[0033] If the deviation data of the repair effect exceeds the deviation threshold range, the weight allocation parameters of the feature dataset are dynamically adjusted based on the degree of deviation, and the repair effect index is recalculated and generated.

[0034] In one embodiment, the preliminary value of the repair effect index is calculated using the following formula:

[0035]

[0036] in, This indicates the preliminary value of the repair effect index. This represents the total number of key ecological characteristic parameters. Indicates the first The importance weights of key ecological characteristic parameters are dynamically allocated based on the degradation risk level. Indicates the first The timing adaptation coefficients of each parameter are dynamically adjusted according to the repair phase. Indicates the first Spatial fit coefficients of each parameter Indicates the first Goodness of fit of time-series trends for each parameter. Indicates the first The spatial heterogeneity of each parameter was calculated using the coefficient of variation method. , Indicates the first The dynamic adaptation coefficients of each parameter's repair target are determined based on preset repair targets. .

[0037] Secondly, this application also provides a system for identifying and restoring ecologically vulnerable areas based on remote sensing imagery, the system comprising:

[0038] The image processing module is used to acquire multi-source remote sensing data, including optical images and synthetic aperture radar images. It calculates correction parameters based on the multi-source remote sensing data to adjust the spatial position of the images. If there are cloud-covered areas, it uses a temporal interpolation method to fill in the pixel values ​​and generate a complete remote sensing image.

[0039] The degradation identification module is used to generate humidity time-series data from the pre-constructed soil moisture inversion model by inputting complete remote sensing images and determine vegetation change trends. It performs correlation analysis and trend matching between vegetation change trends and humidity time-series data to identify ecologically degraded areas.

[0040] The risk assessment module is used to extract the spatiotemporal distribution characteristics of ecologically degraded areas, identify abnormal areas based on the spatiotemporal distribution characteristics, and use spatial interpolation algorithms to interpolate the abnormal areas. It also combines multi-level risk standards to assess the degradation risk level.

[0041] The restoration verification module is used to verify the restoration effect of ecological restoration areas based on the degradation risk level, and to obtain restoration effect indicators by comparing and analyzing the initial monitoring data.

[0042] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0043] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method.

[0044] The aforementioned method, system, computer equipment, and storage medium for identifying and restoring ecologically vulnerable areas based on remote sensing imagery first acquire multi-source remote sensing data, including optical and synthetic aperture radar (SAR) images. Based on this multi-source remote sensing data, spatial correction parameters are calculated to accurately register the spatial locations of the images. For cloud-occupied areas in the images, a temporal interpolation method is used to fill in pixel values, generating a complete remote sensing image without missing data. Next, the complete remote sensing image is input into a preset soil moisture inversion model to generate soil moisture time-series data. Simultaneously, vegetation-related parameters are extracted based on the complete remote sensing image, and vegetation change trends are determined. The vegetation... Correlation analysis and trend matching were performed on the changing trends and soil moisture time series data to accurately delineate ecologically degraded areas. Next, the spatiotemporal distribution characteristics of these ecologically degraded areas were extracted, and anomaly areas were identified based on these characteristics. Spatial interpolation algorithms were used to interpolate the spatiotemporal data of the anomaly areas, and combined with a pre-set multi-level degradation risk classification standard, the risk level of the ecologically degraded areas was quantitatively assessed. Finally, based on the degradation risk level, the restoration effect was verified in the ecological restoration areas. The preliminary assessment value of the restoration effect was compared and analyzed with the initial monitoring data of the corresponding areas to generate standardized restoration effect indicators. This method effectively addresses the issue of insufficient data integrity caused by cloud cover and terrain shadow interference when using multi-source remote sensing data fusion and preprocessing, providing a high-quality data foundation for subsequent ecological parameter inversion. By coupling vegetation change trends with soil moisture time-series data, it replaces traditional static threshold determination methods, improving the accuracy of ecological degradation area identification and the clarity of boundary delineation. Spatial interpolation algorithms optimize anomalous area data, combined with multi-level risk standards, to achieve quantitative assessment of ecological degradation risk levels, providing a scientific basis for prioritizing ecological restoration work. Based on degradation risk levels, restoration effectiveness verification is conducted, enhancing the targeting and effectiveness of ecological restoration work and reducing the cost and time required for manual field investigations. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A flowchart illustrating the method for identifying and restoring ecologically fragile areas based on remote sensing imagery provided in this embodiment of the invention;

[0047] Figure 2 This is a structural block diagram of the system for identifying and restoring ecologically fragile areas based on remote sensing images, provided in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] In one embodiment, such as Figure 1 As shown, this application provides a method for identifying and restoring ecologically vulnerable areas based on remote sensing imagery, which may include the following steps:

[0050] Step S101: Collect multi-source remote sensing data including optical images and synthetic aperture radar images; calculate correction parameters based on multi-source remote sensing data to adjust the spatial position of the images; if there are cloud-covered areas, use temporal interpolation to fill pixel values ​​and generate a complete remote sensing image.

[0051] Specifically, optical imagery can provide detailed information such as surface vegetation spectrum and land use type, while synthetic aperture radar imagery has all-weather, all-time observation capabilities, effectively compensating for the limitations of optical imagery due to weather and lighting conditions. The calculation of correction parameters is based on the sensor parameters of the remote sensing imagery, topographic elevation data, and ground control point coordinates. Through coordinate transformation and geometric correction operations, spatial position deviations caused by sensor attitude, topographic undulations, and other factors from different data sources are eliminated, achieving accurate registration of multi-source imagery. To address the issue of missing pixel values ​​caused by cloud cover in the imagery, continuous time-series cloud-free remote sensing imagery data of the same area is called up, and temporal interpolation methods are used to calculate the pixel values ​​at the missing locations, completing the data completion and ultimately outputting a complete remote sensing imagery with accurate spatial location and no missing data.

[0052] Step S102: Input the complete remote sensing image into the constructed soil moisture inversion model to generate moisture time series data and determine the vegetation change trend. Perform correlation analysis and trend matching on the vegetation change trend and moisture time series data to identify ecologically degraded areas.

[0053] First, complete remote sensing images are input into a soil moisture inversion model. The model calculates characteristic parameters such as the backscattering coefficient and vegetation canopy water content of the images, outputting soil moisture data at different time points in the region. The data from each time point are then integrated to form a soil moisture time-series data. Simultaneously, vegetation-related parameters such as normalized vegetation index and vegetation cover are extracted based on the complete remote sensing images. By analyzing the magnitude and evolution of the changes in each parameter over time, the vegetation change trend is determined. On this basis, a correlation analysis is conducted between the vegetation change trend and the soil moisture time-series data. The correlation coefficient between the two is calculated to clarify the degree of correlation. Trend matching is performed simultaneously to determine whether the changes in vegetation parameters and soil moisture show synergy. Areas where vegetation cover continues to decline and soil moisture decreases significantly at the same time are designated as ecologically degraded areas.

[0054] Step S103: Extract the spatiotemporal distribution characteristics of the ecologically degraded area, determine the abnormal area based on the spatiotemporal distribution characteristics, and use a spatial interpolation algorithm to interpolate the abnormal area. Combine the multi-level risk standards to assess the degradation risk level.

[0055] Specifically, for the delineated ecologically degraded areas, their spatiotemporal distribution characteristics are extracted. Temporal characteristics include degradation rate, degradation duration, and degradation stage classification, while spatial characteristics include the distribution range, spatial clustering, boundary morphology, and spatial correlation with surrounding ecosystems. Based on preset spatiotemporal characteristic thresholds, sub-regions with degradation rates exceeding the normal range and spatial clustering significantly higher than surrounding areas are identified as anomalous regions. For areas within anomalous regions with insufficient data resolution or missing data, spatial interpolation algorithms are used to supplement and optimize their spatiotemporal data, improving spatial continuity and completeness. Finally, the processed spatiotemporal distribution characteristic data is compared item by item with preset multi-level degradation risk standards. Based on differences in indicators such as degradation rate, impact range, and degree of ecosystem damage, ecologically degraded areas are classified into different risk levels.

[0056] Step S104: Verify the restoration effect of the ecological restoration area based on the degradation risk level, and obtain the restoration effect index by comparing and analyzing the initial monitoring data.

[0057] Furthermore, based on the risk level of the ecologically degraded area, the core indicators and verification scope for the restoration effect are determined. For high-risk areas, the verification focus is on core parameters such as vegetation cover restoration rate, soil moisture increase, and ecosystem stability index; for low-risk areas, the verification focus is on the effect of curbing degradation trends and the fluctuation range of ecological parameters. Then, real-time monitoring data of the ecological restoration area is obtained and substituted into a pre-set calculation method to obtain preliminary restoration effect indicators. Based on this, initial monitoring data before the implementation of ecological restoration work is retrieved, and the preliminary restoration effect indicators are compared and analyzed with the initial monitoring data. Through difference calculations and deviation quantification, the changes in each ecological parameter before and after restoration are clarified. Finally, the comparative analysis results are integrated to generate standardized restoration effect indicators, which can intuitively reflect the implementation effectiveness of restoration measures.

[0058] The aforementioned method for identifying and restoring ecologically vulnerable areas based on remote sensing imagery involves collecting multi-source remote sensing data from optical and synthetic aperture radar (SAR), calculating spatial correction parameters to complete image registration, and using temporal interpolation to fill cloud-obscured areas to generate a complete remote sensing image. This complete image is then input into a soil moisture inversion model to generate temporal moisture data. Simultaneously, vegetation parameters are extracted to determine trends, and ecological degradation areas are delineated through correlation analysis and trend matching. The spatiotemporal distribution characteristics of degradation areas are extracted to identify anomalous areas. Spatial interpolation algorithms are used to process anomalous area data, and multi-level risk standards are combined to quantify and assess degradation risk levels. The ecological restoration effect is verified based on risk levels, and standardized restoration effect indicators are generated by comparing initial monitoring data. This method addresses the interference problem from a single data source through multi-source remote sensing data preprocessing, ensuring data integrity; it replaces static threshold judgment with vegetation-moisture coupling analysis, improving the accuracy of degradation area identification; it achieves risk quantification assessment through spatial interpolation and multi-level standards, providing a basis for restoration priority allocation; and it enhances the targeting and effectiveness of restoration while reducing the cost and time required for manual surveys.

[0059] In one embodiment, the spatial position of the image is adjusted by calculating correction parameters based on multi-source remote sensing data. If there are areas obscured by clouds, a temporal interpolation method is used to fill in the pixel values ​​to generate a complete remote sensing image. This may include the following steps:

[0060] Step S201: Extract the image registration error data corresponding to the multi-source remote sensing data, calculate the error distribution matrix using the least squares fitting algorithm on the image registration error data, and generate error correction parameters.

[0061] Step S202: Using error correction parameters, spatial position adjustment is performed on the original optical images and synthetic aperture radar images of the multi-source remote sensing data to generate a multi-source image set after registration optimization.

[0062] Step S203: Perform radiation scale uniform correction and imaging mechanism difference compensation processing on the optical images and synthetic aperture radar images in the multi-source image set, respectively, and output standardized image data.

[0063] Step S204: Based on standardized image data, determine the spectral, texture, and topographic features of multi-source images, and perform time series consistency correction processing on the time series images corresponding to the image features to generate a correction dataset.

[0064] Step S205: Perform radiometric calibration verification preprocessing on the calibration dataset to determine whether the radiometric accuracy meets the preset accuracy threshold. If it does, generate the preprocessed image set.

[0065] Step S206: Perform pixel-level fusion processing on the preprocessed image set based on the weighted fusion algorithm to obtain fused image data.

[0066] Step S207: Detect cloud-occluded areas in the fused image data. If they exist, use temporal interpolation to fill the pixel values ​​in the occluded areas to generate a complete remote sensing image.

[0067] Specifically, firstly, image registration error data corresponding to the multi-source remote sensing data is extracted. A least-squares fitting algorithm is used to calculate this error data, generating an error distribution matrix, and thus obtaining error correction parameters. Then, using these error correction parameters, spatial position adjustments are performed on the original optical images and synthetic aperture radar (SAR) images from the multi-source remote sensing data, generating a registration-optimized multi-source image set. Next, radiometric scale uniformity correction and imaging mechanism difference compensation processing are sequentially performed on the optical images and SAR images in the multi-source image set, respectively, outputting standardized image data. Based on this standardized image data, the spectral characteristics of the multi-source images are extracted. The system incorporates feature, texture, and topographic features, and performs time-series consistency correction on the corresponding temporal image data to generate a corrected dataset. Radiometric calibration and verification preprocessing are then performed on the corrected dataset to determine if its radiometric accuracy meets a preset accuracy threshold. If it does, the preprocessed image set is output. A weighted fusion algorithm is then used to perform pixel-level fusion processing on the preprocessed image set to obtain fused image data. Finally, cloud-occluded areas are detected in the fused image data. If occlusion areas exist, temporal remote sensing image data of the same area is retrieved, and temporal interpolation is used to fill in the pixel values ​​of the occluded areas, generating a complete remote sensing image.

[0068] This embodiment calculates the error distribution matrix and generates error correction parameters using a least-squares fitting algorithm, effectively reducing spatial registration deviation of multi-source remote sensing images and ensuring the consistency of image spatial location. Through radiometric scale uniformity correction and imaging mechanism difference compensation processing, it eliminates data source differences between different types of remote sensing images, achieving data standardization and laying the foundation for subsequent feature extraction. Combined with temporal imagery, it performs time-series consistency correction to ensure the continuity and reliability of image features over time. Radiometric calibration verification preprocessing uses accuracy thresholds to ensure that the radiometric accuracy of the output data meets the requirements of subsequent analysis. Pixel-level weighted fusion enhances the information richness of image data, while temporal interpolation fills the data gaps caused by cloud cover. The final generated complete remote sensing image possesses spatial consistency, data standardization, temporal continuity, and information integrity.

[0069] In one embodiment, the complete remote sensing image is input into the constructed soil moisture inversion model to generate moisture time-series data and determine vegetation change trends. Correlation analysis and trend matching are performed on the vegetation change trends and moisture time-series data to identify ecologically degraded areas. This may include the following steps:

[0070] Step S301: Obtain multi-temporal image data corresponding to the complete remote sensing image, extract near-infrared band reflectance data and red band reflectance data from the multi-temporal image data, perform difference and ratio operations on the band reflectance data, and generate vegetation index data.

[0071] Step S302: Extract time series features based on vegetation index data, and use a preset time series analysis algorithm to perform trend analysis on the time series features to determine the vegetation change trend.

[0072] Step S303: Obtain image data corresponding to the complete remote sensing image, perform inversion operation on the image data based on the preset soil moisture inversion model, and generate soil moisture time series data; the soil moisture inversion model is constructed based on the random forest machine learning algorithm.

[0073] Preferably, the image data corresponding to the acquired complete remote sensing image covers the multi-band information required for soil moisture inversion (such as the backscattering coefficient of synthetic aperture radar imagery and vegetation-related band data of optical imagery). Subsequently, the preset soil moisture inversion model is constructed based on the random forest machine learning algorithm. During the construction process, it has been trained and validated using a large amount of sample data (including measured soil moisture data and corresponding image feature data). Utilizing the powerful nonlinear fitting and multi-feature learning capabilities of the random forest algorithm, it can accurately capture the complex mapping relationship between image features and actual soil moisture. In specific operation, after the acquired image data is feature extracted and standardized according to the model requirements, it is input into the soil moisture inversion model to perform the inversion operation. The model outputs the soil moisture data for a single time node. By sequentially performing the above inversion process on the image data of multiple consecutive time nodes corresponding to the complete remote sensing image, the soil moisture data of each time node is integrated to finally generate soil moisture time series data covering the target area and containing continuous time dimension information. This data can intuitively reflect the evolution pattern of regional soil moisture in the time dimension.

[0074] Step S304: Perform correlation analysis and trend matching on the temporal characteristics of soil moisture time series data and vegetation change trends to determine the ecological evolution status and obtain the corresponding ecological dynamic change pattern.

[0075] Step S305: Determine whether the ecological dynamic change pattern shows a continuous downward trend in vegetation index and soil moisture and is below the preset ecological degradation trend threshold. If so, determine the ecological degradation area.

[0076] Specifically, multi-temporal image data corresponding to the area in the complete remote sensing image is acquired. This multi-temporal image data needs to cover a continuous time series to meet the requirements of time series analysis. Near-infrared band reflectance data and red band reflectance data are accurately extracted from the multi-temporal image data to ensure that the spatial location and time node of the two types of band data correspond one-to-one. The extracted near-infrared band reflectance data and red band reflectance data are processed sequentially by difference and ratio operations. Quantifiable vegetation index data is generated through standardized operation logic. This vegetation index data can intuitively reflect the density of vegetation cover in the region. Based on the generated vegetation index data, time series features are further extracted, including core features such as the change amplitude, fluctuation period, and extreme value occurrence time of the vegetation index. Pre-set time series analysis algorithms (such as linear trend analysis, Mann-Kendall trend test, etc.) are used to conduct systematic trend analysis on the extracted time series features. The algorithm outputs the evolution law of the vegetation index in the time dimension, thereby determining the vegetation change trend. Simultaneously, full-band image data corresponding to complete remote sensing images were acquired and used as input data to a soil moisture inversion model constructed based on a random forest machine learning algorithm. The model learns the mapping relationship between image features and actual soil moisture in the sample data, performs inversion operations on the input image data, and outputs soil moisture data at different time points, integrating them to form continuous soil moisture time series data. Subsequently, time series features (such as moisture change rate, stable interval, etc.) corresponding to the soil moisture time series data are extracted and correlated with time series features corresponding to the determined vegetation change trends. First, correlation analysis is carried out by calculating correlation coefficients to clarify the correlation strength and positive and negative correlation between soil moisture and vegetation change. Then, trend matching processing is performed to compare the consistency between the two in terms of change direction, change rate, and abrupt change time points. Based on the results of correlation analysis and trend matching, the regional ecological evolution status is comprehensively determined, resulting in various types of ecological dynamic change patterns, including "vegetation-moisture synergistic improvement", "vegetation-moisture synergistic degradation", and "vegetation improvement-moisture decline". Finally, a preset ecological degradation trend threshold is set, which is determined based on regional historical ecological baseline data and ecological protection targets. It is then determined whether the ecological dynamic change pattern shows the characteristic of simultaneous and continuous decline in vegetation index and soil moisture, and whether the decline rate and duration of both are lower than the preset ecological degradation trend threshold. If so, the region is clearly determined to be an ecologically degraded region.

[0077] This embodiment accurately extracts key band data from multi-temporal images and performs standardized calculations to generate vegetation indices, ensuring the reliability and comparability of vegetation growth status representation. A soil moisture inversion model is constructed based on the random forest machine learning algorithm. Leveraging the algorithm's powerful nonlinear fitting and feature learning capabilities, the accuracy and stability of soil moisture inversion in complex land cover environments are improved, making it more adaptable to diverse land cover types compared to traditional linear inversion methods. By conducting coupled analysis of the temporal characteristics of soil moisture and vegetation change trends, the correlation between the two is clarified, and a comprehensive assessment of ecological evolution status is achieved, effectively avoiding misjudgments of degraded areas caused by single ecological parameter analysis. Based on ecological degradation trend thresholds set according to actual regional conditions, the quantitative delineation of ecologically degraded areas is realized, clarifying the criteria for determining degraded areas and improving the consistency and repeatability of identification results across different regions.

[0078] In one embodiment, the spatiotemporal distribution characteristics of ecologically degraded areas are extracted, abnormal areas are identified based on these characteristics, and spatial interpolation algorithms are used to interpolate the abnormal areas. The degradation risk level is then assessed using a multi-level risk standard. This process may include the following steps:

[0079] Step S401: Obtain the trend parameters of ecological degradation areas, and perform outlier removal and data normalization on the trend parameters in sequence to generate a standardized trend dataset.

[0080] Step S402: Perform time series feature fitting on the trend dataset using a fitting algorithm, and perform spatial feature clustering analysis on the fitted dataset using a clustering algorithm to integrate and obtain spatiotemporal distribution features.

[0081] Step S403: Determine whether the spatiotemporal distribution characteristics exceed the preset distribution threshold range. If they do, generate the corresponding alarm signal data.

[0082] Step S404: Based on the threshold triggering conditions of the alarm signal data, extract the abnormal distribution features in the ecological degradation area that completely match the alarm threshold, and generate an abnormal area identifier.

[0083] Step S405: Use a spatial interpolation algorithm to interpolate the spatiotemporal data corresponding to the abnormal area identifier to generate the spatiotemporal change pattern of the ecological degradation area. Combined with the preset multi-level degradation risk level classification standard, the degradation risk level is quantitatively assessed.

[0084] Specifically, the process begins by acquiring trend parameters of ecologically degraded areas, including core indicators such as degradation rate, degradation magnitude, and degradation duration. These trend parameters are then subjected to outlier removal and data normalization. Outlier removal utilizes statistical testing methods to remove extreme interference values, while data normalization eliminates dimensional differences between parameters through standardization transformations, ultimately generating a standardized trend dataset. Subsequently, a fitting algorithm (such as linear or nonlinear fitting) is used to fit the time-series features of the standardized trend dataset, revealing the evolution patterns and trend characteristics of the parameters over time. Based on the fitted dataset, a clustering algorithm (such as K-means clustering) is used to perform spatial feature clustering analysis, dividing the data into sub-regions with similar spatial distribution patterns. The time-series feature fitting results are then integrated with the spatial feature clustering results to obtain spatiotemporal distribution characteristics that encompass both temporal evolution patterns and spatial distribution patterns. Next, a threshold range for the spatiotemporal distribution characteristics is preset, determined based on historical ecological data and ecological security benchmark values ​​for the region. The integrated spatiotemporal distribution characteristics are then assessed to determine if they exceed the preset threshold range. If they do, corresponding alarm signal data is generated to clarify the type and severity of the abnormal features. Based on this, and using the threshold triggering conditions of the alarm signal data, abnormal distribution features that perfectly match the alarm threshold are extracted from the ecologically degraded areas, generating unique abnormal area identifiers for the areas corresponding to these features. Finally, a spatial interpolation algorithm is used to interpolate the spatiotemporal data corresponding to the abnormal area identifiers, supplementing missing data and improving the spatial continuity of the data, generating a complete spatiotemporal change pattern of the ecologically degraded areas. This spatiotemporal change pattern is then compared item by item with a preset multi-level degradation risk classification standard (covering indicators such as degradation rate, impact range, and degree of harm) to quantitatively assess the degradation risk level corresponding to each ecologically degraded area.

[0085] This embodiment effectively improves data quality by performing outlier removal and normalization on trend parameters, eliminating dimensional differences and extreme value interference, and providing a standardized and highly reliable data foundation for subsequent analysis. Through the integration of temporal feature fitting and spatial feature clustering analysis, it achieves comprehensive and accurate extraction of the spatiotemporal distribution characteristics of ecologically degraded areas, overcoming the limitations of single-dimensional analysis and clearly presenting the temporal evolution and spatial pattern of the degradation process. Based on the judgment of preset threshold intervals and the generation of alarm signals, it achieves rapid identification and early warning of abnormal features, providing clear guidance for subsequent abnormal area location. The application of spatial interpolation algorithms compensates for the lack of spatiotemporal data, improves the integrity and continuity of data, and ensures the accuracy of spatiotemporal change patterns. Combined with multi-level degradation risk classification standards for quantitative assessment, it achieves refined classification of degradation risks, providing a scientific basis for prioritizing ecological restoration work and developing targeted restoration plans. It reduces subjective errors caused by human intervention and improves the efficiency and objectivity of ecological degradation risk assessment.

[0086] In one embodiment, verifying the restoration effect of the ecological restoration area based on the degradation risk level, and obtaining restoration effect indicators by comparing and analyzing initial monitoring data, may include the following steps:

[0087] Step S501: Extract key ecological feature parameters associated with the corresponding ecological restoration area based on the degradation risk level, and generate a feature dataset.

[0088] Step S502: Perform time-series trend fitting and spatial heterogeneity calculation on each key ecological feature parameter of the feature dataset, and combine them with the preset restoration target dynamic adaptation coefficient for weighted fusion to generate preliminary values ​​of restoration effect indicators.

[0089] Preferably, each key ecological characteristic parameter in the feature dataset is used as an independent analysis unit, and two core processes are carried out: First, time-series trend fitting, based on continuous monitoring data of each parameter during the restoration period, linear or nonlinear fitting algorithms are used to extract the evolution law of the parameters over time (such as recovery rate, stability trend, etc.), and the time-series trend fitting results are output; Second, spatial heterogeneity measurement, using quantitative methods such as the coefficient of variation method, the degree of distribution difference of each parameter in different spatial locations within the restoration area is calculated, and spatial heterogeneity data is output. The smaller the data value, the more uniform the spatial distribution of the parameter. Subsequently, the preset restoration target dynamic adaptation coefficient is retrieved. This coefficient is determined according to the preset target of the restoration area (such as vegetation cover restoration threshold, soil moisture improvement standard, etc.), and different restoration targets correspond to different coefficient values. Finally, based on the preset restoration target dynamic adaptation coefficient as the weight allocation basis, the time-series trend fitting results of each parameter and the spatial heterogeneity measurement results are weighted and fused to form a preliminary value of the restoration effect index that can comprehensively reflect the "time dimension restoration trend" and "spatial dimension distribution uniformity".

[0090] Step S503: Perform difference calculation and deviation quantification analysis between the preliminary values ​​of the restoration effect index and the initial ecological monitoring data of the corresponding restoration area to generate restoration effect deviation data.

[0091] Furthermore, the initial ecological monitoring data of the restoration area, i.e., the baseline data collected on key ecological characteristic parameters of the area before ecological restoration work, is used to characterize the ecological baseline state before restoration. Two data processing operations are performed sequentially: first, difference calculation, which calculates the absolute difference between the preliminary value of the restoration effect index and the corresponding parameter baseline value in the initial ecological monitoring data, clarifying the absolute difference between the two; second, deviation quantification analysis, which calculates quantitative indicators such as the relative deviation rate based on the absolute difference and the baseline value of the initial ecological monitoring data, accurately characterizing the relative deviation degree between the preliminary value and the baseline value. Through these two processes, the "difference between the preliminary assessment result and the restoration baseline" is transformed into quantifiable data on the deviation of the restoration effect.

[0092] Step S504: If the deviation data of the repair effect exceeds the deviation threshold range, the weight allocation parameters of the feature dataset are dynamically adjusted based on the degree of deviation, and the repair effect index is recalculated and generated.

[0093] First, based on the assessed degradation risk level, corresponding ecological restoration areas are matched, and key ecological characteristic parameters closely related to the restoration effectiveness of these areas (such as vegetation cover recovery rate, soil moisture increase, and changes in soil organic matter content) are extracted. This ensures that the parameters correspond one-to-one with the core influencing factors of the degradation risk level, thereby generating a feature dataset. Subsequently, time-series trend fitting and spatial heterogeneity calculation are performed on each key ecological characteristic parameter in the feature dataset: time-series trend fitting is used to obtain the evolution pattern and recovery trend of the parameters within the restoration period, while spatial heterogeneity calculation is used to assess the spatial distribution uniformity of the parameters within the restoration area. Combined with a preset dynamic adaptation coefficient for restoration targets (dynamically adjusted according to the restoration stage and type to adapt to the weight requirements of different restoration targets), the time-series fitting results and spatial heterogeneity calculation results are weighted and fused to generate preliminary values ​​for the restoration effect indicators. Next, initial ecological monitoring data before the restoration work in the corresponding restoration area is retrieved. The difference between the preliminary values ​​of the restoration effect indicators and the initial ecological monitoring data is calculated to clarify the absolute difference between the two. Then, the relative deviation is calculated through deviation quantification analysis to generate restoration effect deviation data. Finally, a preset threshold range for the deviation of the restoration effect is set (determined based on the restoration target requirements and industry standards). It is then determined whether the data on the deviation of the restoration effect exceeds this threshold range. If it does, the weight allocation parameters of each key ecological feature parameter in the feature dataset are dynamically adjusted according to the magnitude of the deviation. The weighted fusion operation is then re-executed to generate the corrected restoration effect index.

[0094] This embodiment extracts key ecological characteristic parameters based on degradation risk levels, ensuring the relevance and specificity of the characteristic dataset and laying a precise data foundation for evaluating restoration effectiveness. Through a two-dimensional analysis of time-series trend fitting and spatial heterogeneity measurement, combined with weighted fusion of dynamic adaptation coefficients, it overcomes the limitations of single-dimensional assessment, improving the scientific rigor and comprehensiveness of preliminary restoration effectiveness indicators. By analyzing the deviation between preliminary restoration effectiveness indicators and initial monitoring data, it achieves precise verification of restoration effectiveness, clarifying the differences between the assessment results and expected goals. The adoption of standardized computational and analytical logic reduces subjective errors in manual assessment, providing a quantitative basis for optimizing and adjusting restoration plans, while simultaneously improving the efficiency and objectivity of ecological restoration effectiveness evaluation.

[0095] In one embodiment, the preliminary value of the repair effect index can be calculated using the following formula:

[0096]

[0097] in, This indicates the preliminary value of the repair effect index. This represents the total number of key ecological characteristic parameters. Indicates the first The importance weights of key ecological characteristic parameters are dynamically allocated based on the degradation risk level. Indicates the first The timing adaptation coefficients of each parameter are dynamically adjusted according to the repair phase. Indicates the first Spatial fit coefficients of each parameter Indicates the first Goodness of fit of time-series trends for each parameter. Indicates the first The spatial heterogeneity of each parameter was calculated using the coefficient of variation method. , Indicates the first The dynamic adaptation coefficients of each parameter's repair target are determined based on preset repair targets. .

[0098] This embodiment addresses the poor adaptability of traditional fixed-weight assessments, improving the relevance of the assessment results. The design of taking the reciprocal of spatial heterogeneity in the formula ensures that "the more uniform the spatial distribution," the higher the "contribution value," guaranteeing that the variable logic aligns with the direction of restoration effect evaluation and avoiding calculation ambiguity. Each variable can be quantified using monitoring data or preset parameters, ensuring the repeatability and verifiability of the assessment process. The preliminary values ​​of the restoration effect indicators obtained based on this formula provide a precise quantitative foundation for subsequent deviation analysis and weight optimization, effectively improving the scientific rigor and accuracy of ecological restoration effect assessment.

[0099] In one embodiment, such as Figure 2 As shown, this application also provides a system for identifying and restoring ecologically fragile areas based on remote sensing imagery. The system may include:

[0100] The image processing module 601 is used to acquire multi-source remote sensing data, including optical images and synthetic aperture radar images, calculate correction parameters based on the multi-source remote sensing data to adjust the spatial position of the images, and use temporal interpolation methods to fill pixel values ​​to generate complete remote sensing images if there are cloud-covered areas.

[0101] The degradation identification module 602 is used to generate humidity time series data from the complete remote sensing image input into the constructed soil moisture inversion model and determine the vegetation change trend. It performs correlation analysis and trend matching on the vegetation change trend and humidity time series data to identify ecological degradation areas.

[0102] The risk assessment module 603 is used to extract the spatiotemporal distribution characteristics of ecologically degraded areas, identify abnormal areas based on the spatiotemporal distribution characteristics, interpolate the abnormal areas using a spatial interpolation algorithm, and assess the degradation risk level in combination with multi-level risk standards.

[0103] The restoration verification module 604 is used to verify the restoration effect of the ecological restoration area based on the degradation risk level, and to obtain restoration effect indicators by comparing and analyzing the initial monitoring data.

[0104] The aforementioned system for identifying and restoring ecologically fragile areas based on remote sensing imagery comprises two modules. The image processing module is responsible for acquiring multi-source remote sensing data, including optical and synthetic aperture radar (SAR) images. Based on this data, it calculates spatial correction parameters to accurately adjust the spatial location of the images. Simultaneously, it detects cloud cover areas and uses temporal interpolation to fill in missing pixel values ​​if occlusion is present, ultimately generating a complete remote sensing image that provides a high-quality data foundation for subsequent analysis. The degradation identification module receives the complete remote sensing image output from the image processing module and inputs it into a pre-defined soil moisture inversion model to generate temporal moisture data. It also extracts vegetation-related parameters from the complete remote sensing image and determines vegetation change trends. Correlation analysis and trend analysis are then conducted between the vegetation change trends and the temporal moisture data. The system performs several steps: First, it accurately identifies and outputs ecologically degraded areas. Second, it uses a risk assessment module to obtain the ecologically degraded areas defined by the degradation identification module, extracts the spatiotemporal distribution characteristics of these areas (including temporal evolution patterns and spatial distribution patterns), identifies abnormal areas based on preset feature thresholds, supplements and optimizes the spatiotemporal data of abnormal areas using spatial interpolation algorithms, and then quantitatively assesses and outputs the degradation risk level of each degraded area based on preset multi-level degradation risk classification standards. Third, it uses a restoration verification module based on the degradation risk levels output by the risk assessment module to conduct targeted verification of the restoration effects in the ecological restoration areas. It retrieves the initial ecological monitoring data for the corresponding areas, compares and analyzes the preliminary assessment results of the restoration effects with the initial monitoring data, and finally generates and outputs standardized restoration effect indicators.

[0105] In this embodiment, the image processing module effectively solves the problems of interference and missing data from a single data source by integrating and preprocessing multi-source remote sensing data, ensuring the reliability of data for subsequent analysis. The degradation identification module replaces the traditional single-parameter judgment method with coupled analysis of dual ecological parameters, improving the accuracy of ecological degradation area identification and the clarity of boundary delineation. The risk assessment module achieves quantitative classification of degradation risk through spatiotemporal feature analysis and spatial interpolation optimization, combined with multi-level risk standards, providing a scientific basis for prioritizing ecological restoration work and formulating plans. The restoration verification module conducts differentiated verification based on risk levels, ensuring the pertinence and accuracy of restoration effect assessment through comparative analysis with initial monitoring data. The collaborative work of all modules not only ensures the professionalism of each stage of processing but also achieves seamless data flow, reducing subjective errors caused by human intervention and improving the efficiency and scientific nature of the entire process of ecologically fragile area governance.

[0106] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0107] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method for identifying and restoring ecologically vulnerable areas based on remote sensing imagery as described above.

[0108] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0109] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0110] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for identifying and restoring ecologically vulnerable areas based on remote sensing imagery, characterized in that, The method includes: Multi-source remote sensing data, including optical images and synthetic aperture radar images, are collected. Correction parameters are calculated based on the multi-source remote sensing data to adjust the spatial position of the images. If there are cloud-obscured areas, a temporal interpolation method is used to fill the pixel values ​​to generate a complete remote sensing image. The complete remote sensing image is input into the constructed soil moisture inversion model to generate moisture time series data and determine the vegetation change trend. Correlation analysis and trend matching are performed on the vegetation change trend and moisture time series data to identify ecologically degraded areas. Extract the spatiotemporal distribution characteristics of the ecologically degraded areas, identify abnormal areas based on the spatiotemporal distribution characteristics, and use a spatial interpolation algorithm to interpolate the abnormal areas. Combine this with a multi-level risk standard to assess the degradation risk level. The restoration effect of the ecological restoration area is verified based on the degradation risk level, and the restoration effect index is obtained by comparing and analyzing the initial monitoring data.

2. The method according to claim 1, characterized in that, The step of calculating correction parameters based on the multi-source remote sensing data to adjust the spatial position of the image, and using temporal interpolation to fill pixel values ​​and generate a complete remote sensing image if there are cloud-obscured areas, includes: Extract the image registration error data corresponding to the multi-source remote sensing data, calculate the error distribution matrix using the least squares fitting algorithm on the image registration error data, and generate error correction parameters. The error correction parameters are used to perform spatial position adjustment on the original optical images and synthetic aperture radar images of the multi-source remote sensing data to generate a multi-source image set with registration optimization. The optical images and synthetic aperture radar images in the multi-source image set are subjected to radiation scale uniform correction and imaging mechanism difference compensation processing, respectively, and standardized image data is output. Based on the standardized image data, the spectral, texture, and topographic features of the multi-source images are determined, and time series consistency correction processing is performed on the time series images corresponding to the image features to generate a correction dataset. The calibration dataset is preprocessed with radiometric calibration verification to determine whether the radiometric accuracy meets the preset accuracy threshold. If it does, a preprocessed image set is generated. The preprocessed image set is subjected to pixel-level fusion processing based on a weighted fusion algorithm to obtain fused image data. The cloud-occluded areas in the fused image data are detected. If they exist, a temporal interpolation method is used to fill the pixel values ​​of the occluded areas to generate a complete remote sensing image.

3. The method according to claim 1, characterized in that, The process involves inputting the complete remote sensing image into the constructed soil moisture inversion model to generate moisture time-series data and determine vegetation change trends. Correlation analysis and trend matching are then performed on the vegetation change trends and moisture time-series data to identify ecologically degraded areas, including: Acquire multi-temporal image data corresponding to the complete remote sensing image, extract near-infrared band reflectance data and red band reflectance data from the multi-temporal image data, perform difference and ratio operations on the band reflectance data, and generate vegetation index data. Based on the vegetation index data, time series features are extracted, and a preset time series analysis algorithm is used to perform trend analysis on the time series features to determine the vegetation change trend. The image data corresponding to the complete remote sensing image is obtained, and the image data is processed by inversion operation based on the preset soil moisture inversion model to generate soil moisture time series data; the soil moisture inversion model is constructed based on the random forest machine learning algorithm. Correlation analysis and trend matching are performed on the temporal characteristics of the soil moisture time series data and the vegetation change trend to determine the ecological evolution status and obtain the corresponding ecological dynamic change pattern. Determine whether the ecological dynamic change pattern shows a continuous downward trend in vegetation index and soil moisture and is below a preset ecological degradation trend threshold; if so, identify the ecological degradation area.

4. The method according to claim 1, characterized in that, The process involves extracting the spatiotemporal distribution characteristics of the ecologically degraded areas, identifying anomalous areas based on these characteristics, interpolating the anomalous areas using a spatial interpolation algorithm, and assessing the degradation risk level using a multi-level risk standard. This includes: Obtain the trend parameters of the ecological degradation area, and perform outlier removal and data normalization on the trend parameters in sequence to generate a standardized trend dataset. The trend dataset is fitted with a fitting algorithm to perform time-series feature fitting, and the fitted dataset is subjected to spatial feature clustering analysis using a clustering algorithm to integrate and obtain spatiotemporal distribution features. Determine whether the spatiotemporal distribution characteristics exceed a preset distribution threshold range; if they do, generate corresponding alarm signal data. Based on the threshold triggering conditions of the alarm signal data, extract the abnormal distribution features in the ecologically degraded area that perfectly match the alarm threshold, and generate an abnormal area identifier; Spatial interpolation algorithms are used to interpolate the spatiotemporal data corresponding to the abnormal area identifiers to generate the spatiotemporal change pattern of the ecological degradation area. Combined with the preset multi-level degradation risk level classification standard, the degradation risk level is quantitatively assessed.

5. The method according to claim 1, characterized in that, The verification of the restoration effect of the ecological restoration area based on the degradation risk level, combined with the initial monitoring data for comparative analysis, yields restoration effect indicators, including: Based on the degradation risk level, key ecological feature parameters associated with the corresponding ecological restoration area are extracted to generate a feature dataset; For each key ecological feature parameter in the feature dataset, time-series trend fitting and spatial heterogeneity calculation are performed respectively. Combined with the preset restoration target dynamic adaptation coefficient weighted fusion, preliminary values ​​of restoration effect indicators are generated. The preliminary values ​​of the restoration effect indicators are compared with the initial ecological monitoring data of the corresponding restoration area by performing difference calculation and deviation quantification analysis to generate restoration effect deviation data. If the deviation data of the repair effect exceeds the deviation threshold range, the weight allocation parameters of the feature dataset are dynamically adjusted based on the degree of deviation, and the repair effect index is recalculated and generated.

6. The method according to claim 5, characterized in that, The preliminary value of the repair effect index is calculated using the following formula: in, This indicates the preliminary value of the repair effect index. This represents the total number of key ecological characteristic parameters. Indicates the first The importance weights of key ecological characteristic parameters are dynamically allocated based on the degradation risk level. Indicates the first The timing adaptation coefficients of each parameter are dynamically adjusted according to the repair phase. Indicates the first Spatial fit coefficients of each parameter Indicates the first Goodness of fit of time-series trends for each parameter. Indicates the first The spatial heterogeneity of each parameter was calculated using the coefficient of variation method. , Indicates the first The dynamic adaptation coefficients of each parameter's repair target are determined based on preset repair targets. .

7. A system for identifying and restoring ecologically fragile areas based on remote sensing imagery, characterized in that, The system includes: The image processing module is used to acquire multi-source remote sensing data, including optical images and synthetic aperture radar images, calculate correction parameters based on the multi-source remote sensing data to adjust the spatial position of the images, and use a time-series interpolation method to fill pixel values ​​and generate a complete remote sensing image if there are cloud-covered areas. The degradation identification module is used to input the complete remote sensing image into the constructed soil moisture inversion model to generate moisture time series data and determine the vegetation change trend. It performs correlation analysis and trend matching on the vegetation change trend and the moisture time series data to identify ecological degradation areas. The risk assessment module is used to extract the spatiotemporal distribution characteristics of the ecological degradation area, determine the abnormal area based on the spatiotemporal distribution characteristics, interpolate the abnormal area using a spatial interpolation algorithm, and assess the degradation risk level in combination with multi-level risk standards. The restoration verification module is used to verify the restoration effect of the ecological restoration area based on the degradation risk level, and to obtain restoration effect indicators by comparing and analyzing the initial monitoring data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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