A remote sensing identification method and device for ecological restoration areas

Abstract

The present invention belongs to the field of remote sensing vegetation detection technology and provides a remote sensing identification method and device for ecological restoration areas. The method comprises: obtaining remote sensing data and measured data of a target area; obtaining NDVRI index images at different times in the historical period based on the remote sensing data; the NDVRI index is a normalized sand-vegetation restoration index; an increase in the NDVRI index indicates an expansion of the bare soil or sand area, and a decrease in the NDVRI index indicates an increase in vegetation cover; constructing a prediction model using NDVRI index images at different times in the historical period to predict the NDVRI index at time t in the monitoring phase; and screening breakpoints where the difference between the predicted NDVRI index and the measured data is less than 0 to identify ecological restoration areas. The present invention is suitable for vegetation monitoring in sandy environments. By integrating brightness and greenness components, it emphasizes the combined characteristics of vegetation restoration and bare land reduction, thereby improving the accuracy and reliability of sandy ecological monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing vegetation monitoring, and in particular to a remote sensing identification method and device for an ecological restoration area. Background Art

[0002] Desertification, a prominent environmental issue in grassland regions, has become an urgent ecological crisis. To curb the harm of windblown sand and protect the sandy ecosystem, ecological restoration projects centered on revegetation have been widely implemented. These projects have played a significant role in mitigating regional desertification and promoting ecological restoration in sandy areas. Furthermore, establishing scientific and effective ecological restoration monitoring methods is crucial for evaluating restoration results and optimizing subsequent management strategies.

[0003] Traditional methods for monitoring vegetation changes primarily fall into two categories: ground-based measurements and remote sensing monitoring. While ground-based measurement methods, such as visual estimation, sampling, and photography, offer high accuracy, they are limited by human and material resources, making it difficult to obtain comprehensive information on sand restoration at a regional scale. In contrast, remote sensing monitoring, with its wide coverage and high temporal resolution, has gradually become the primary means of monitoring ecological restoration. In remote sensing time-series data, surface changes are often influenced by a combination of seasonal, trend, and sudden changes, and are also subject to interference factors such as atmospheric scattering, cloud shadow effects, and geometric errors. Therefore, effectively determining and identifying surface changes within complex remote sensing time-series data has become a key issue in remote sensing time-series analysis.

[0004] Remote sensing time series analysis methods have developed rapidly in recent years, resulting in a variety of technical approaches, including thresholding, differencing, trajectory segmentation, classification, statistics, and regression. These methods each have advantages in terms of time series data frequency, spectral index type, detection unit, and real-time performance, but they also have limitations. For example, trajectory segmentation algorithms (such as LandTrendr) primarily use the univariate spectral index (NBR) to detect land surface change. However, these algorithms rely on annual mean data, making it difficult to capture continuous changes and exhibiting poor real-time performance. Thresholding algorithms (such as VCT) use the univariate spectral index (IFZ) to identify changes, but are similarly limited by the low time series frequency. While the CCDC algorithm, which combines statistics and classification, uses multivariate spectral input and can detect land cover type changes in near real time, the multivariate input is prone to error, and the complexity of time series data also affects its detection accuracy. Summary of the Invention

[0005] In response to the defects in the existing technology, the present invention provides a remote sensing identification method and device for ecological restoration areas to solve the current problem of limitations in the detection of vegetation cover changes in sandy areas, where the accuracy is affected by the index performance and the temporal limitations of the index data set used.

[0006] In a first aspect, the present invention provides a method for remote sensing identification of ecological restoration areas, comprising:

[0007] Obtain remote sensing data and measured data of the target area;

[0008] Based on the remote sensing data, NDVRI index images are obtained at different times in historical stages; the NDVRI index is a normalized sand-vegetation restoration index. When the NDVRI index increases, it indicates that the scope of bare soil or sand is expanding, and when the NDVRI index decreases, it indicates that vegetation cover is increasing;

[0009] A prediction model is constructed using NDVRI index images at different times in the historical stage to predict the NDVRI index at time t in the monitoring stage;

[0010] The breakpoints where the variation of the NDVRI index is less than 0 are screened out to determine the ecological restoration area.

[0011] It can be seen from the above technical solution that the present invention provides a remote sensing identification method for ecological restoration areas, and proposes a normalized sand-vegetation restoration index combined with tasseled cap transformation. This index captures the dynamic changes of vegetation recovery and ground reduction during sand restoration by integrating brightness and greenness components. It can effectively identify the temporal and spatial characteristics of dynamic changes in restoration, and provides a novel and efficient technical means for the scientific evaluation and dynamic monitoring of sand ecological restoration.

[0012] Optionally, acquiring remote sensing data of the target area includes:

[0013] Collect all available Sentinel-2 data in the target area in the B1-B12 and B8A bands;

[0014] Splice multiple images acquired at the same time into a remote sensing image that completely covers the target area;

[0015] Converting the remote sensing data value of the remote sensing image into a reflectivity value;

[0016] Based on the model, the effects of atmospheric scattering and absorption are removed to obtain the surface reflectance value.

[0017] Optionally, the NDVRI index is determined based on the surface reflectance value after tasseled cap transformation, and is specifically calculated according to the following formula:

[0018] , where B is the brightness component of the surface reflectance value after tasseled cap transformation, and G is the greenness component of the surface reflectance value after tasseled cap transformation.

[0019] Optionally, the method of constructing a prediction model using NDVRI index images at different times in historical stages to predict the NDVRI index at time t in the monitoring stage includes:

[0020] A regression model is constructed based on historical data; the regression model is used to obtain the predicted value of the NDVRI index at time t. ;

[0021] Get the moving residual and the moving residual in the sliding time window during the monitoring phase , determine whether a breakpoint occurs;

[0022] When the breakpoint is determined, the actual observation value at the breakpoint and the predicted value of the NDVRI index are calculated. The difference between them determines the magnitude of the change;

[0023] Based on the magnitude of the change, the ecological restoration area is determined.

[0024] Optionally, the regression model is constructed using the ordinary least squares method, and the NDVRI index prediction value at time t is Specifically

[0025] ,

[0026] in, is the intercept coefficient estimated based on historical period observations, represents the long-term trend term, k represents the harmonic term order, is the amplitude, To harmonize the phases of the seasons, is the frequency, the sine function describes the seasonal fluctuations, is the random error at time t.

[0027] Optionally, the moving residual and The MOSUM method is used to determine the specific calculation according to the following formula:

[0028] ,

[0029] in, is the estimated value of the variance, n is the number of observations in the historical period, h is the size of the sliding window, h and n are fixed ratios, and t represents the time point.

[0030] Optionally, the method of screening out the breakpoints where the variation between the predicted NDVRI index and the measured data is less than 0 and determining the ecological restoration area is specifically as follows:

[0031] The change range is based on the predicted value of the NDVRI index at time t and the remote sensing measured value at time t Determine that the variation range is ;

[0032] When the change amplitude mc is less than 0, it is determined to be an ecological restoration area.

[0033] In a second aspect, the present invention provides a remote sensing identification device for an ecological restoration area, comprising:

[0034] Acquisition module, used to obtain remote sensing data and measured data of the target area;

[0035] An index processing module is used to obtain NDVRI index images at different times in historical stages based on the remote sensing data; the NDVRI index is a normalized sand-vegetation restoration index. When the NDVRI index increases, it indicates that the scope of bare soil or sand is expanding, and when the NDVRI index decreases, it indicates that vegetation cover is increasing;

[0036] A prediction module is used to construct a prediction model using NDVRI index images at different times in the historical stage to predict the NDVRI index at time t in the monitoring stage;

[0037] The restoration determination module is used to screen out the breakpoints where the change between the predicted NDVRI index and the measured data is less than 0, and determine the ecological restoration area.

[0038] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above methods when executing the computer program.

[0039] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having computer program instructions stored thereon, which implement the steps of any of the above methods when executed by a processor.

[0040] By adopting the above technical solution, this application has the following beneficial effects:

[0041] This paper proposes a normalized sand-vegetation restoration index (NDVRI) based on the tasseled cap transform, and combines it with a prediction model to dynamically monitor the time series changes of sand restoration. The NDVRI index emphasizes the comprehensive characteristics of vegetation restoration and bare land reduction by integrating brightness (B) and greenness (G) components, showing significant advantages in spatial accuracy and temporal response characteristics, and reducing the impact of the high reflectivity of the sand background on the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0043] Figure 1 A flowchart of a remote sensing identification method for ecological restoration areas provided by an embodiment of the present invention is shown;

[0044] Figure 2 A schematic diagram showing an ecological restoration area identified by the NDVRI index provided in an embodiment of the present invention is shown;

[0045] Figure 3 A schematic diagram showing the ecological restoration time identified by the NDVRI index provided in an embodiment of the present invention is shown;

[0046] Figure 4 A schematic diagram showing the average hysteresis time of the NDVRI index provided by an embodiment of the present invention is shown;

[0047] Figure 5 A schematic diagram of a remote sensing identification device for ecological restoration areas provided by an embodiment of the present invention is shown;

[0048] Figure 6 A schematic diagram of an electronic device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0049] The following embodiments of the technical solution of the present invention will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are therefore only examples and are not intended to limit the scope of protection of the present invention.

[0050] It should be noted that, unless otherwise specified, the technical or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0051] Currently, various indices are limited in their ability to describe low-coverage green vegetation in arid and semi-arid regions. This makes it difficult to accurately reflect actual changes during the assessment and monitoring of vegetation restoration in sandy areas, as they are easily affected by the background of bare land. This approach addresses this issue and proposes a vegetation change detection method specifically for sandy environments to address the shortcomings of traditional methods and improve the accuracy and reliability of sandy ecological monitoring.

[0052] like Figure 1 As shown, this embodiment provides a remote sensing identification method for ecological restoration areas, including:

[0053] S1. Obtain remote sensing data and measured data of the target area.

[0054] S2. Based on remote sensing data, obtain NDVRI index images at different times throughout history. The NDVRI index is the Normalized Difference Sand-Vegetation Restoration Index. An increase in the NDVRI index indicates an expansion in the extent of bare soil or sand, while a decrease in the NDVRI index indicates an increase in vegetation cover.

[0055] During the dataset construction process, this step first calculated the NDVRI index value for each image, and then used the pixel median method to synthesize monthly NDVRI index images. The historical phase refers to the period before ecological restoration projects were carried out, while the monitoring phase refers to the period after ecological restoration projects were already underway.

[0056] At the same time, in order to highlight the superiority of the NDVRI index, a time series data set of the traditional vegetation index NDVI and SAVI was constructed for comparison. The formula is as follows:

[0057] ,

[0058] Among them, NIR and RED represent the reflectance of the near-infrared and red bands, respectively.

[0059] The SAVI (Soil Adjustment Factor) formula is:

[0060] ,

[0061] Where L is the adjustment factor (usually 0.5).

[0062] Before predicting the NDVRI index based on the forecast model, in order to further improve the quality and continuity of the time series data, the Savitzky-Golay filter (SG filter) is used to smooth the synthesized monthly time series data.

[0063] S3. Use the NDVRI index images at different times in the historical period to build a prediction model and predict the NDVRI index at time t in the monitoring period.

[0064] S4. Filter out breakpoints where the NDVRI index change is less than 0 and determine the ecological restoration area.

[0065] Optionally, obtaining remote sensing data of the target area in S1 includes:

[0066] S101. Collect all available Sentinel-2 data in the target area in the B1-B12 and B8A bands.

[0067] S102. Stitching multiple images acquired at the same time into a remote sensing image that completely covers the target area.

[0068] S103. Convert the remote sensing data values of the remote sensing image into reflectivity values.

[0069] This step is used to convert the value obtained by the sensor into the reflectivity value or radiance value required in this embodiment. Generally, the DN value is obtained using remote sensing technology, so the conversion is performed in this step.

[0070] S104. Remove the effects of atmospheric scattering and absorption based on a model (such as 6S or FLAASH) to obtain the surface reflectance value.

[0071] The data in step S1 includes remote sensing data and measured data. The remote sensing data is used to collect the B1-B12 and B8A bands of all available sentinel data in the target area. The measured data is the location and time of the area that has undergone ecological restoration.

[0072] Alternatively, the NDVRI index is determined based on the surface reflectance value after tasseled cap transformation, and is specifically calculated according to the following formula:

[0073] , where B is the brightness component of the surface reflectance value after tasseled cap transformation, and G is the greenness component of the surface reflectance value after tasseled cap transformation.

[0074] The NDVRI index significantly enhances the contrast signal between bare soil and vegetation through the difference between brightness and greenness components, thereby improving the ability to identify dynamic changes in sand. The index uses a ratio form, and by weighting the denominator, it balances the numerical differences in brightness and greenness, effectively reducing fluctuations caused by environmental factors such as imaging angle and atmospheric conditions. During the sand restoration process, the dynamic changes in brightness and greenness components reflect the process of transformation from bare soil to vegetation cover. Specifically, when the NDVRI index rises, it usually indicates that the scope of bare soil or sand has expanded and the surface brightness component has increased; when the index decreases, it indicates that vegetation cover has increased and the greenness component is dominant.

[0075] Specifically, the surface reflectance values are transformed through a Tasseled Cap Transformation (TCT), also known as the KT Transformation, to obtain the brightness and greenness components used in calculating the NDVRI index in this step. The Tasseled Cap Transformation (TCT), also known as the KT Transformation, is a classic linear transformation method used to compress and reconstruct multispectral pixel values in remote sensing images. Because the Tasseled Cap Transformation coefficients depend on the sensor's band characteristics, its parameters are generally not directly applicable to different sensors. To ensure the reliability of the results, this example referenced multiple coefficient derivation methods, ultimately adopting the Sentinel-2 Tasseled Cap Transformation coefficients derived by Roumen Nedkov et al. Typically, the main components extracted by the Tasseled Cap Transformation include brightness (TCB), greenness (TCG), and wetness (TCW), which represent surface reflectance characteristics, vegetation cover, and soil moisture levels, respectively. In this example, only the greenness and brightness components are used. The coefficients used to calculate each component are shown in Table 1.

[0076] Table 1 Coefficients used in sentinel 2 tasseled cap transformation

[0077]

[0078] Table 1 (continued)

[0079]

[0080] Optionally, step S3 includes:

[0081] S301. Build a regression model based on historical data; the regression model is used to obtain the predicted value of the NDVRI index at time t .

[0082] Specifically, the regression model is constructed using the ordinary least squares method, and the predicted value of the NDVRI index at time t is Specifically

[0083] ,

[0084] in, is the intercept coefficient estimated based on historical period observations, represents the long-term trend term, k represents the harmonic term order, is the amplitude, To harmonize the phases of the seasons, is the frequency, the sine function describes the seasonal fluctuations, is the random error at time t. The regression model includes trend and seasonal terms to adapt to the diversity of time series.

[0085] S302. Obtain the moving residual and the moving residual in the sliding time window in the monitoring phase , determine whether a breakpoint occurs;

[0086] Optionally, the moving residual and The MOSUM method is used to determine the specific calculation according to the following formula:

[0087] ,

[0088] in, is the estimated value of the variance, n is the number of observations in the historical period, h is the size of the sliding window, h and n are fixed ratios, and t represents the time point.

[0089] when Exceeding the significance level , you can determine that a breakpoint has occurred.

[0090] S303. When a breakpoint is determined, calculate the actual observed value at the breakpoint and the NDVRI index predicted value The difference between them determines the magnitude of the change;

[0091] S304. Determine the ecological restoration area based on the extent of change.

[0092] Optionally, the breakpoints where the variation between the predicted NDVRI index and the measured data is less than 0 are selected to determine the ecological restoration area, specifically:

[0093] The change range is based on the predicted value of the NDVRI index at time t and the remote sensing measured value at time t OK, the change range is ;

[0094] When the change amplitude mc is less than 0, it is determined to be an ecological restoration area.

[0095] In the formula is the actual observed value at time t. To ensure comparability of the results, a second-order (k=2) harmonic model was used for all parameters. The bandwidth of the MOSUM detection process was set to h=0.25, and the significance level was set to α=0.05. In this example, since we focus only on ecological restoration scenarios, the NDVRI only considers cases where the power outage magnitude mc is less than 0, while the SAVI and NDVI indices only consider cases where the power outage magnitude is greater than 0.

[0096] Figure 2-3 Based on the method provided in this embodiment, a specific analysis is conducted by taking West Ujimqin Banner as an example. Figure 2 The ecological restoration area identified by the NDVRI index, Figure 3The period of ecological restoration identified by the NDVRI index is 2016-2019 for the historical period and 2020-2024 for the monitoring period.

[0097] The method provided in this embodiment is simulated and verified as follows.

[0098] (1) Spatial accuracy evaluation

[0099] The overall accuracy (OA), intersection over union (IOU), recall (REC), precision (PRE), and F1 score (F1) are considered horizontally as evaluation indicators to verify the performance effect of the method provided in this embodiment.

[0100] In the change detection task, a higher precision indicates a lower error rate, and a higher recall indicates a lower missed detection rate. OA, IOU, and F1 are comprehensive indicators of index performance, with higher values indicating better performance. The specific descriptions of each evaluation indicator are as follows:

[0101]

[0102]

[0103]

[0104]

[0105]

[0106] Among them, TP is the number of correctly classified restored pixels, TN is the number of correctly classified unrestored pixels, FP is the number of incorrectly classified restored pixels, and FN is the number of incorrectly classified unrestored pixels. The spatial accuracy results are shown in Table 2:

[0107] Table 2 Comparison of spatial accuracy of different indexes

[0108]

[0109] (2) Time accuracy evaluation

[0110] To quantify and compare the lag time of different remote sensing indices in the ecological restoration process, we used a lag time curve method. Specifically, we used multiple vegetation indices (NDVRI, SAVI, and NDVI) to summarize the change years in the ecological restoration area and calculated the lag time for each pixel. The lag time was defined as the time difference between the actual restoration year and the year when the index change was detected. The formula is as follows:

[0111]

[0112] in, is the year of change detected using remote sensing indices, It is the actual year of ecological restoration.

[0113] At the same time, in order to further quantify the time response characteristics of different indices, the average pixel lag time of each index is calculated, as shown in Table 3. Figure 4 As shown in the figure, the average lag time of the normalized sand-vegetation restoration index NDVRI is significantly lower than that of SAVI and NDVI, and the time response characteristics of NDVRI are better than those of SAVI and NDVI indices.

[0114] Table 3 Statistics of average lag time of each index

[0115]

[0116] Experimental results show that the NDVRI index's overall accuracy (86.0%) and intersection-over-union (IoU) ratio (73.5%) are superior to those of SAVI (84.0%, 65.4%) and NDVI (78.4%, 55.8%). In terms of temporal response, the NDVRI's average lag time is only 1.8 years, surpassing SAVI (2.3 years) and NDVI (3.1 years), demonstrating greater sensitivity to changes in the early stages of restoration and greater spatial consistency.

[0117] In one embodiment, a remote sensing identification device 50 for an ecological restoration area is provided. Figure 5 As shown, including:

[0118] Acquisition module 501, used to obtain remote sensing data and measured data of the target area;

[0119] The index processing module 502 is used to obtain NDVRI index images at different times in the historical stage based on the remote sensing data. The NDVRI index is a normalized sand-vegetation restoration index. When the NDVRI index increases, it indicates that the scope of bare soil or sand is expanding. When the NDVRI index decreases, it indicates that vegetation cover is increasing.

[0120] The prediction module 503 is used to construct a prediction model using NDVRI index images at different times in the historical stages, and predict the NDVRI index at time t in the monitoring stage;

[0121] The restoration determination module 504 is used to screen out the breakpoints where the variation between the predicted NDVRI index and the measured data is less than 0, and determine the ecological restoration area.

[0122] The ecological restoration area remote sensing identification device 50 provided in the embodiment of the present application adopts the same inventive concept as the above-mentioned ecological restoration area remote sensing identification method and can achieve the same beneficial effects, which will not be repeated here.

[0123] Based on the same inventive concept as the above-mentioned ecological restoration area remote sensing identification method, the embodiment of the present application further provides an electronic device 60, such as Figure 6 As shown, the electronic device 60 may include a processor 601 and a memory 602 .

[0124] The processor 601 can implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of the present invention. The steps of the methods disclosed in the embodiments of the present invention can be directly implemented as a hardware processor, or can be implemented by a combination of hardware and software modules in the processor.

[0125] Memory 602, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs, and modules. Memory can include at least one type of storage medium. Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. Memory 602 in embodiments of the present invention can also be a circuit or any other device capable of performing a storage function, used to store program instructions and / or data.

[0126] An embodiment of the present invention provides a computer-readable storage medium for storing computer program instructions for the above-mentioned electronic device, which includes a program for executing the above-mentioned ecological restoration area remote sensing identification method.

[0127] The above embodiments are only used to provide a detailed introduction to the technical solutions of the present application. However, the description of the above embodiments is only used to help understand the methods of the embodiments of the present invention and should not be understood as limiting the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art should be included in the scope of protection of the embodiments of the present invention.

Claims

1. A remote sensing identification method for ecological restoration areas, characterized in that: include: Obtain remote sensing data and measured data of the target area; For the remote sensing data, NDVRI index images were obtained at different times in the historical period. The NDVRI index is the normalized sand-vegetation restoration index. When the NDVRI index increases, it indicates that the range of bare soil or sand has expanded, and when the NDVRI index decreases, it indicates that vegetation cover has increased. The NDVRI index is determined based on the surface reflectance value after tasseled cap transformation and is specifically calculated according to the following formula: , where B is the brightness component of the surface reflectance value after tasseled cap transformation, and G is the greenness component of the surface reflectance value after tasseled cap transformation; A prediction model is constructed using NDVRI index images at different times in the historical stage to predict the NDVRI index at time t in the monitoring stage; including: A regression model is constructed based on historical data; the regression model is used to obtain the predicted value of the NDVRI index at time t. ; Get the moving residual and the moving residual in the sliding time window during the monitoring phase , determine whether a breakpoint occurs; the moving residual and The MOSUM method is used to determine the specific calculation according to the following formula: ， in, is the estimated value of the variance, n is the number of observations in the historical period, h is the size of the sliding window, h and n are fixed ratios, and t represents the time point; When the breakpoint is determined, the actual observation value at the breakpoint and the predicted value of the NDVRI index are calculated. The difference between them determines the magnitude of the change; Determine the ecological restoration area based on the magnitude of the change; The breakpoints where the change between the predicted NDVRI index and the measured data is less than 0 are screened out to determine the ecological restoration area.

2. The method according to claim 1, characterized in that The acquiring of remote sensing data of the target area includes: Collect all available Sentinel-2 data in the target area in the B1-B12 and B8A bands; Splice multiple images acquired at the same time into a remote sensing image that completely covers the target area; Converting the remote sensing data value of the remote sensing image into a reflectivity value; Based on the model, the effects of atmospheric scattering and absorption are removed to obtain the surface reflectance value.

3. The method according to claim 2, characterized in that The regression model is constructed using the ordinary least squares method, and the NDVRI index prediction value at time t Specifically ， in, is the intercept coefficient estimated based on historical period observations, represents the long-term trend term, k represents the harmonic term order, is the amplitude, To harmonize the phases of the seasons, is the frequency, the sine function describes the seasonal fluctuations, is the random error at time t.

4. The method according to claim 3, characterized in that The breakpoints where the variation between the predicted NDVRI index and the measured data is less than 0 are screened out to determine the ecological restoration area, specifically: The change range is based on the predicted value of the NDVRI index at time t and the remote sensing measured value at time t Determine that the variation range is ; When the change amplitude mc is less than 0, it is determined to be an ecological restoration area.

5. A remote sensing identification device for ecological restoration areas, characterized in that: include: Acquisition module, used to obtain remote sensing data and measured data of the target area; The index processing module is used to obtain NDVRI index images at different times in the historical stage based on the remote sensing data. The NDVRI index is a normalized sand-vegetation restoration index. When the NDVRI index increases, it indicates that the scope of bare soil or sand is expanding. When the NDVRI index decreases, it indicates that vegetation cover is increasing. The NDVRI index is determined based on the surface reflectance value after tasseled cap transformation and is specifically calculated according to the following formula: , where B is the brightness component of the surface reflectance value after tasseled cap transformation, and G is the greenness component of the surface reflectance value after tasseled cap transformation; The prediction module is used to construct a prediction model using NDVRI index images at different times in the historical stage to predict the NDVRI index at time t in the monitoring stage; it includes: A regression model is constructed based on historical data; the regression model is used to obtain the predicted value of the NDVRI index at time t. ; Get the moving residual and the moving residual in the sliding time window during the monitoring phase , determine whether a breakpoint occurs; the moving residual and The MOSUM method is used to determine the specific calculation according to the following formula: ， in, is the estimated value of the variance, n is the number of observations in the historical period, h is the size of the sliding window, h and n are fixed ratios, and t represents the time point; When the breakpoint is determined, the actual observation value at the breakpoint and the predicted value of the NDVRI index are calculated. The difference between them determines the magnitude of the change; Determine the ecological restoration area based on the magnitude of the change; The restoration determination module is used to screen out the breakpoints where the change between the predicted NDVRI index and the measured data is less than 0, and determine the ecological restoration area.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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