A Deep Learning-Based Method and System for Remote Sensing Data Analysis of Soil Erosion

Abstract

This invention discloses a method and system for analyzing soil erosion telemetry data based on deep learning, belonging to the field of data analysis technology. The method includes: extracting soil loss data, surface runoff data, and topographic data; fusing the soil loss data, surface runoff data, and topographic data, and obtaining soil erosion risk index data and soil erosion rate data through feature extraction technology; calculating the weight values ​​of the soil erosion risk index data and soil erosion rate data using feature evaluation technology; and constructing a soil erosion prediction model based on the soil erosion risk index data, soil erosion rate data, and weight values ​​using a deep learning framework and spatiotemporal analysis algorithm. This invention, through telemetry data collection and feature extraction, can quickly obtain key soil erosion information, simplifying the data processing workflow and ensuring the accuracy and reliability of the data.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and more specifically, to a method and system for analyzing remote sensing data on soil erosion based on deep learning. Background Technology

[0002] Soil erosion is a complex environmental problem, referring to the erosion of surface soil and rock by natural forces such as water flow, wind, gravity, and freeze-thaw cycles, accompanied by material migration and deposition. This process involves multiple erosion mechanisms and leads to a significant decline in land productivity. Therefore, accurate and efficient monitoring and analysis of soil erosion are crucial for solving this problem.

[0003] In recent years, with the development of remote sensing technology, monitoring soil erosion over large areas using image data collected by platforms such as satellites or drones has become an important method, providing real-time and continuous data coverage. However, traditional methods of remote sensing data analysis, relying on manual interpretation and basic statistical methods, have limitations in deeply mining data information and are inefficient. To address these issues, deep learning-based methods have been introduced into the analysis of remote sensing data on soil erosion. This method automatically extracts important features from images using deep learning algorithms, thereby achieving more accurate identification and quantitative assessment.

[0004] Despite this, current soil erosion monitoring equipment is mainly concentrated in key ecological areas and large watersheds, while the coverage of monitoring networks remains insufficient in urban fringe areas, small watersheds, and remote regions. This uneven distribution of data collection leads to an incomplete understanding of soil erosion in these specific areas, thus limiting the ability to develop effective control measures for these regions. Furthermore, due to technological limitations, some monitoring equipment may produce false alarms or misses, which not only affects the accuracy of the data but may also mislead subsequent soil and water conservation strategy planning, ultimately impacting the accuracy of prediction models and analysis results.

[0005] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0006] In response to the problems in related technologies, this invention proposes a method and system for analyzing soil erosion remote sensing data based on deep learning, in order to overcome the aforementioned technical problems existing in the existing related technologies.

[0007] Therefore, the specific technical solution adopted by the present invention is as follows:

[0008] According to one aspect of the present invention, a method for analyzing soil erosion telemetry data based on deep learning is provided, the method comprising the following steps:

[0009] S1. Collect telemetry data of the soil erosion monitoring area, and extract soil loss data, surface runoff data and topographic data based on the telemetry data;

[0010] S2. Soil loss data, surface runoff data and topographic data are integrated, and water and soil loss risk index data and soil erosion rate data are obtained through feature extraction technology.

[0011] S3. Calculate the weight values ​​of the soil erosion risk index data and soil erosion rate data using feature evaluation technology;

[0012] S4. Based on the soil erosion risk index data, soil erosion rate data and weight values, a soil erosion prediction model is constructed using a deep learning framework and spatiotemporal analysis algorithm.

[0013] S5. Use soil and water loss prediction models to predict the status of soil and water loss, and formulate soil and water loss prevention and control measures based on the prediction results.

[0014] Furthermore, collecting telemetry data from the soil erosion monitoring area and extracting soil loss data, surface runoff data, and topographic data based on the telemetry data includes the following steps:

[0015] S11. Obtain telemetry data of the soil erosion monitoring area through a satellite remote sensing platform;

[0016] S12. Using the geographic reference points and geometric registration technology of the geographic information system, the telemetry data is geographically corrected, and atmospheric correction software is applied to process the geographically corrected telemetry data.

[0017] S13. Based on the processed telemetry data, compare the changes in vegetation cover and bare surface area in different time series, and obtain soil loss data in the monitoring area through the differential vegetation index algorithm.

[0018] S14. Extract water body information from the processed telemetry data, monitor the change in surface water area, and combine rainfall data with hydrological models to obtain surface runoff data for the monitored area.

[0019] S15. Use remote sensing technology to generate digital elevation model data, and combine it with terrain analysis algorithms to calculate and extract terrain data of the monitored area.

[0020] Furthermore, using geographic reference points and geometric registration techniques from a geographic information system, the telemetry data is geographically corrected, and atmospheric correction software is applied to process the geographically corrected telemetry data, including the following steps:

[0021] S121. Import the acquired telemetry data into the geographic information system, and select the map projection and coordinates according to the geographic characteristics of the monitoring area;

[0022] S122. Identify and mark ground control points with known coordinates as geographic reference points in the geographic information system, and select the pixel positions corresponding to the geographic reference points in the telemetry data to establish control point pairs.

[0023] S123. Perform affine transformation based on control point pairs to perform geographic correction on telemetry data, and then resample the geographically corrected telemetry data.

[0024] S124. Use atmospheric correction software to perform radiometric calibration on the resampled remote sensing data, converting the values ​​of each band in the remote sensing data into atmospheric radiance values.

[0025] S125. Use atmospheric correction software to process the radiance value and restore it to the surface reflectance value.

[0026] Furthermore, based on the processed telemetry data, the changes in vegetation cover and bare surface area at different time series are compared, and soil loss data in the monitoring area are obtained through the differential vegetation index algorithm, including the following steps:

[0027] S131. Based on the processed telemetry data, the telemetry data for each period are calculated using the normalized vegetation index formula.

[0028] S132. Set a threshold based on the calculated normalized vegetation index, distinguish between vegetation-covered areas and bare surface areas using the threshold, and vectorize the vegetation-covered areas and bare surface areas into planar layers.

[0029] S133. In the geographic information system, vegetation cover layers and bare surface layers from different periods are overlaid to identify areas of change and to calculate the changes in vegetation cover area and bare surface area in each period.

[0030] S134. Based on statistical changes, analyze the changing trends of vegetation cover and bare surface area;

[0031] S135. Select one of the telemetry data from different periods as the baseline data, and use the differential vegetation index algorithm to calculate the differential vegetation index between the baseline data and the telemetry data of each period.

[0032] S136. Using a pre-constructed soil erosion assessment model and combined with the differential vegetation index, estimate the soil loss data in the monitoring area.

[0033] S137. Integrate the estimated soil loss data and trends to obtain soil loss data for the monitoring area at different times.

[0034] Furthermore, the formula for the Normalized Difference Vegetation Index is:

[0035]

[0036] In the formula, NDVI represents the normalized vegetation index;

[0037] NIR represents near-infrared reflectance;

[0038] RED indicates the reflectivity in the red light band.

[0039] Furthermore, by integrating soil loss data, surface runoff data, and topographic data, and using feature extraction techniques to obtain soil erosion risk index data and soil erosion rate data, the following steps are included:

[0040] S21. Using principal component analysis, soil loss data, surface runoff data, and topographic data are integrated into a dataset.

[0041] S22. Extract the characteristics of soil erosion from the dataset. The characteristics of soil erosion include the average soil loss, average surface runoff, and the fluctuation of soil loss over different time periods.

[0042] S23. Calculate the soil and water loss risk index based on the average soil loss and average surface runoff over different time periods, combined with topographic data.

[0043] S24. Calculate soil erosion rate data based on the fluctuation of soil loss over different time periods and in conjunction with topographic data.

[0044] Furthermore, the calculation of weight values ​​for the soil erosion risk index data and soil erosion rate data using feature assessment techniques includes the following steps:

[0045] S31. Extract feature data from soil and water loss risk index data and soil erosion rate data, and combine the extracted feature data into a feature matrix;

[0046] S32. Based on the analysis requirements, define the soil erosion risk index and soil erosion rate as target variables, and use statistical algorithms to calculate the correlation between each feature and the target variables to obtain the feature correlation coefficient.

[0047] S33. Normalize the feature correlation coefficients to a preset interval, and assign corresponding weight values ​​to each feature based on the normalized feature correlation coefficient scores.

[0048] Furthermore, based on soil erosion risk index data, soil erosion rate data, and weight values, a soil erosion prediction model is constructed using a deep learning framework and spatiotemporal analysis algorithm, including the following steps:

[0049] S41. Use a deep learning framework to perform time series analysis on the soil erosion risk index data and soil erosion rate data to detect whether there is periodic data in the data. If there is periodic data, apply a time series decomposition algorithm to remove the periodic data. If there is no periodic data, continue to analyze the periodic changes in the data.

[0050] S42. Based on the soil erosion risk index data and soil erosion rate data after removing periodic data, the amplitude and phase information of each frequency component are extracted as feature vectors using beamforming technology in spatiotemporal analysis algorithms.

[0051] S43. Based on the obtained feature vectors, evaluate the significance of each frequency component through statistical testing, and screen based on the evaluation results to construct a periodic prediction model.

[0052] S44. The remaining data after removing periodic data is used as random fluctuation data, and a fluctuation prediction model is built using a deep learning framework.

[0053] S45. Integrate and superimpose the periodic prediction model, the fluctuation prediction model, and the weight values ​​to obtain the soil and water loss prediction model.

[0054] Furthermore, the formula for the soil erosion prediction model is as follows:

[0055]

[0056] Where, This represents the predicted value of soil erosion;

[0057] y1 represents the predicted value of the periodic prediction model;

[0058] ω1 represents the weight values ​​of the periodic prediction model;

[0059] y2 represents the predicted value from the volatility prediction model;

[0060] ω2 represents the weight value of the fluctuation prediction model.

[0061] According to another aspect of the present invention, a deep learning-based remote sensing data analysis system for soil erosion is also provided. The system includes a data collection and extraction module, a data fusion and feature extraction module, a feature evaluation and weight calculation module, a model building module, and a prediction and prevention strategy formulation module.

[0062] The data collection and extraction module is used to collect telemetry data of the soil erosion monitoring area and extract soil loss data, surface runoff data and topographic data based on the telemetry data;

[0063] The data fusion and feature extraction module is used to fuse soil loss data, surface runoff data and topographic data, and obtain soil and water loss risk index data and soil erosion rate data through feature extraction technology.

[0064] The feature assessment and weight calculation module is used to calculate the weight values ​​of soil erosion risk index data and soil erosion rate data using feature assessment technology.

[0065] The model building module is used to build a soil erosion prediction model based on soil erosion risk index data, soil erosion rate data and weight values, using a deep learning framework and spatiotemporal analysis algorithm.

[0066] The prediction and prevention strategy formulation module is used to predict the status of soil erosion using a soil erosion prediction model and formulate soil erosion prevention and control measures based on the prediction results.

[0067] The beneficial effects of this invention are as follows:

[0068] 1. This invention can quickly obtain key information on soil and water loss through telemetry data collection and feature extraction. This not only simplifies the data processing process but also ensures the accuracy and reliability of the data, laying a solid foundation for subsequent analysis and prediction.

[0069] 2. This invention constructs a soil erosion prediction model that not only has strong learning and generalization capabilities, but also fully considers the changing factors in time and space, thereby achieving accurate prediction of soil erosion status. This provides strong technical support for formulating targeted prevention and control measures and helps to more effectively address the problem of soil erosion.

[0070] 3. The present invention, through the soil and water conservation measures formulated based on the prediction results, can implement precise policies for the characteristics of soil and water loss in different regions and time periods. It can not only effectively curb the worsening trend of soil and water loss, but also promote the protection and restoration of the ecological environment and achieve sustainable development. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a flowchart of a method for analyzing soil erosion telemetry data based on deep learning according to an embodiment of the present invention;

[0073] Figure 2This is a schematic diagram of a deep learning-based remote sensing data analysis system for soil erosion according to an embodiment of the present invention.

[0074] In the picture:

[0075] 1. Data collection and extraction module; 2. Data fusion and feature extraction module; 3. Feature evaluation and weight calculation module; 4. Model building module; 5. Prediction and prevention strategy formulation module. Detailed Implementation

[0076] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0077] According to an embodiment of the present invention, a method and system for analyzing remote sensing data of soil erosion based on deep learning are provided.

[0078] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, the method for analyzing soil erosion telemetry data based on deep learning according to an embodiment of the present invention includes the following steps:

[0079] S1. Collect telemetry data of the soil erosion monitoring area, and extract soil loss data, surface runoff data and topographic data based on the telemetry data.

[0080] Specifically, collecting telemetry data from the soil erosion monitoring area and extracting soil loss data, surface runoff data, and topographic data based on the telemetry data includes the following steps:

[0081] S11. Obtain telemetry data of the soil erosion monitoring area through a satellite remote sensing platform.

[0082] It should be noted that the telemetry data includes remote sensing images, surface reflectance, vegetation index, and topographic data.

[0083] S12. Using geographic reference points and geometric registration technology of geographic information systems, perform geographic correction on telemetry data, and apply atmospheric correction software to process the geographically corrected telemetry data.

[0084] Specifically, the process of using geographic reference points and geometric registration techniques from a geographic information system to perform geographic correction on telemetry data, and then applying atmospheric correction software to process the geographically corrected telemetry data includes the following steps:

[0085] S121. Import the acquired telemetry data into the geographic information system, and select the map projection and coordinates according to the geographic characteristics of the monitoring area;

[0086] S122. Identify and mark ground control points with known coordinates as geographic reference points in the geographic information system, and select the pixel positions corresponding to the geographic reference points in the telemetry data to establish control point pairs.

[0087] S123. Perform affine transformation based on control point pairs to perform geographic correction on telemetry data, and then resample the geographically corrected telemetry data.

[0088] S124. Use atmospheric correction software to perform radiometric calibration on the resampled remote sensing data, converting the values ​​of each band in the remote sensing data into atmospheric radiance values.

[0089] S125. Use atmospheric correction software to process the radiance value and restore it to the surface reflectance value.

[0090] S13. Based on the processed telemetry data, compare the changes in vegetation cover and bare surface area in different time series, and obtain soil loss data in the monitoring area through the differential vegetation index algorithm.

[0091] Specifically, based on the processed telemetry data, the changes in vegetation cover and bare surface area over different time series are compared, and soil loss data for the monitored area are obtained using the differential vegetation index algorithm, including the following steps:

[0092] S131. Based on the processed telemetry data, the telemetry data for each period is calculated using the normalized vegetation index formula.

[0093] Specifically, the formula for the Normalized Difference Vegetation Index is:

[0094]

[0095] In the formula, NDVI represents the normalized vegetation index;

[0096] NIR represents near-infrared reflectance;

[0097] RED indicates the reflectivity in the red light band.

[0098] It should be noted that NDVI values ​​typically range from -1 to +1, but in most cases, the value will be between 0 and 1; positive values ​​indicate vegetation cover, and the closer the value is to +1, the more lush the vegetation; negative values ​​may indicate water bodies or clouds; values ​​close to 0 may represent bare soil or other non-vegetated surfaces.

[0099] S132. Set a threshold based on the calculated normalized vegetation index, distinguish between vegetation-covered areas and bare surface areas using the threshold, and vectorize the vegetation-covered areas and bare surface areas into planar layers.

[0100] S133. In the geographic information system, vegetation cover layers and bare surface layers from different periods are overlaid to identify areas of change and to calculate the changes in vegetation cover area and bare surface area for each period.

[0101] S134. Based on statistical changes, analyze the changing trends of vegetation cover and bare surface area.

[0102] S135. Select one of the telemetry data from different periods as the baseline data, and use the differential vegetation index algorithm to calculate the differential vegetation index between the baseline data and the telemetry data of each period.

[0103] S136. Using a pre-constructed soil erosion assessment model and combined with the differential vegetation index, estimate the soil loss data in the monitoring area.

[0104] It should be noted that the formula for the soil and water loss assessment model is: A = R * K * LS * C * P; where A represents the average annual soil loss (unit: tons / hectare·year); R represents the rainfall erosivity factor, reflecting the rainfall intensity and the energy of raindrops hitting the ground; K represents the soil erodibility factor, determined based on soil type and texture; LS represents the slope length and gradient factor, describing the impact of topographic features on erosion; C represents the crop management factor, reflecting the impact of vegetation cover on the erosion process; and P represents the soil and water conservation measures factor, considering the control effect of human activities such as terraces and vegetation belts on erosion.

[0105] By combining the results of the differential vegetation index with other relevant geographic information data, the C factor can be adjusted to more accurately estimate soil loss; for example, in areas with increased vegetation cover, the C factor may decrease, thereby reducing the predicted soil loss.

[0106] S137. Integrate the estimated soil loss data and trends to obtain soil loss data for the monitoring area at different times.

[0107] S14. Extract water body information from the processed telemetry data, monitor the change in surface water area, and combine rainfall data with hydrological models to obtain surface runoff data for the monitored area.

[0108] It should be further explained that a hydrological model is a model that uses mathematical or physical methods to simulate hydrological processes. It can simulate and predict the formation and change of surface runoff based on input hydrological data (such as rainfall, topography, vegetation cover, etc.). In soil and water loss monitoring, hydrological models are mainly used to calculate surface runoff, thereby assessing the risk and degree of soil and water loss.

[0109] S15. Use remote sensing technology to generate digital elevation model data, and combine it with terrain analysis algorithms to calculate and extract terrain data of the monitored area.

[0110] It should be further explained that a digital elevation model (DEM) is a type of spatial geographic information data that uses limited terrain elevation data to digitally simulate ground topography, i.e., a digital representation of terrain surface morphology. It is a core data system for terrain analysis in Geographic Information Systems (GIS), providing quantitative analysis of terrain features such as slope, aspect, and curvature. Furthermore, terrain data includes terrain type, terrain relief, and terrain texture.

[0111] S2. By integrating soil loss data, surface runoff data, and topographic data, and using feature extraction technology, we obtain soil and water loss risk index data and soil erosion rate data.

[0112] Specifically, the process of integrating soil loss data, surface runoff data, and topographic data, and obtaining soil erosion risk index data and soil erosion rate data through feature extraction techniques includes the following steps:

[0113] S21. Using principal component analysis, soil loss data, surface runoff data, and topographic data are integrated into a dataset.

[0114] S22. Extract the characteristics of soil erosion from the dataset. The characteristics of soil erosion include the average soil loss, average surface runoff, and the fluctuation of soil loss over different time periods.

[0115] S23. Calculate the soil and water loss risk index based on the average soil loss and average surface runoff over different time periods, combined with topographic data.

[0116] S24. Calculate soil erosion rate data based on the fluctuation of soil loss over different time periods and in conjunction with topographic data.

[0117] S3. Calculate the weight values ​​of the soil erosion risk index data and soil erosion rate data using feature evaluation technology.

[0118] Specifically, the steps for calculating the weights of soil erosion risk index data and soil erosion rate data using feature assessment techniques include:

[0119] S31. Extract feature data from soil and water loss risk index data and soil erosion rate data, and combine the extracted feature data into a feature matrix;

[0120] S32. Based on the analysis requirements, define the soil erosion risk index and soil erosion rate as target variables, and use statistical algorithms to calculate the correlation between each feature and the target variables to obtain the feature correlation coefficient.

[0121] S33. Normalize the feature correlation coefficients to a preset interval, and assign corresponding weight values ​​to each feature based on the normalized feature correlation coefficient scores.

[0122] S4. Based on the soil erosion risk index data, soil erosion rate data and weight values, a soil erosion prediction model is constructed using a deep learning framework and spatiotemporal analysis algorithm.

[0123] Specifically, based on soil erosion risk index data, soil erosion rate data, and weight values, the construction of a soil erosion prediction model using a deep learning framework and spatiotemporal analysis algorithm includes the following steps:

[0124] S41. Use a deep learning framework to perform time series analysis on the soil erosion risk index data and soil erosion rate data to detect whether there is periodic data in the data. If there is periodic data, apply a time series decomposition algorithm to remove the periodic data. If there is no periodic data, continue to analyze the periodic changes in the data.

[0125] S42. Based on the soil erosion risk index data and soil erosion rate data after removing periodic data, the amplitude and phase information of each frequency component are extracted as feature vectors using beamforming technology in spatiotemporal analysis algorithms.

[0126] S43. Based on the obtained feature vectors, evaluate the significance of each frequency component through statistical testing, and screen based on the evaluation results to construct a periodic prediction model.

[0127] It should be noted that the periodic forecasting model is built upon the time series forecasting model, and the formula for the periodic forecasting model is:

[0128]

[0129] In the formula, y represents the periodic prediction result; m represents the number of frequency components considered; i represents a natural number; ω i Represents the frequency component f i The weights; A(f) i ) represents the frequency component f i The amplitude of f; i Represents the frequency component; t represents the time point; P(f i) represents the frequency component f i The phase.

[0130] S44. The remaining data after removing periodic data is used as random fluctuation data, and a fluctuation prediction model is built using a deep learning framework.

[0131] It should be noted that the volatility prediction model is built upon a recurrent neural network model, and the formula for the volatility prediction model is:

[0132]

[0133] In the formula, h t The hidden state represents the hidden layer output at time t; LSTM represents a recurrent neural network model; v t-1 h represents the eigenvector of the previous time step t-1; t-1 This represents the hidden state at the previous time step t-1; denoted by t, W represents the predicted output at time t; W represents the weight matrix; and b represents the bias vector.

[0134] S45. Integrate and superimpose the periodic prediction model, the fluctuation prediction model, and the weight values ​​to obtain the soil and water loss prediction model.

[0135] Specifically, the formula for the soil erosion prediction model is as follows:

[0136]

[0137] Where, This represents the predicted value of soil erosion;

[0138] y1 represents the predicted value of the periodic prediction model;

[0139] ω1 represents the weight values ​​of the periodic prediction model;

[0140] y2 represents the predicted value from the volatility prediction model;

[0141] ω2 represents the weight value of the fluctuation prediction model.

[0142] S5. Use soil and water loss prediction models to predict the status of soil and water loss, and formulate soil and water loss prevention and control measures based on the prediction results.

[0143] It should be further explained that using soil erosion prediction models to predict soil erosion and formulating soil erosion prevention and control measures based on the prediction results includes:

[0144] Step 1: Analysis of Model Prediction Results

[0145] The soil and water loss prediction model is used to predict the target area and obtain prediction results, including a distribution map of soil and water loss risk levels and predicted values ​​of soil erosion rate. The prediction results are analyzed to identify high-risk areas, low-risk areas, and hotspots with high soil erosion rates.

[0146] Step Two: Preliminary Planning of Prevention and Control Measures:

[0147] Based on the forecast results, preliminary prevention and control measures are planned, including engineering measures (such as building terraces, silt-retaining dams, and silt-retaining dams), biological measures (such as afforestation and grass planting), and agricultural farming measures (such as changing farming methods, crop rotation, and fallow). The priority of prevention and control measures is determined, and high-risk areas and hotspots with high soil erosion rates should be given priority.

[0148] Step 3: Detailed Design of Prevention and Control Measures:

[0149] The preliminary prevention and control measures should be designed in detail, including the specific implementation locations, implementation methods, required materials, and budget estimates. Natural conditions such as topography, climate, and soil, as well as local socio-economic conditions, should be taken into account to ensure the scientific validity and feasibility of the prevention and control measures.

[0150] Step 4: Implementation and Supervision of Prevention and Control Measures:

[0151] Implement the detailed prevention and control measures plan to ensure that all measures are completed on time, with quality, and in quantity; establish a supervision mechanism to supervise and inspect the implementation process of the prevention and control measures to ensure their effective execution.

[0152] Step 5: Effectiveness Evaluation and Feedback Adjustment

[0153] After a period of time, the effectiveness of prevention and control measures should be evaluated, including changes in soil and water loss and the degree of reduction in soil erosion rate. Based on the evaluation results, the prevention and control measures should be adjusted to optimize the plan and improve the effectiveness.

[0154] Step Six: Continuous Monitoring and Dynamic Management

[0155] Establish a continuous monitoring mechanism to regularly monitor and assess the status of soil erosion, and promptly identify new problems and changes; based on the monitoring results, dynamically adjust prevention and control measures to ensure the continuity and effectiveness of soil erosion prevention and control work.

[0156] like Figure 2 As shown, according to another embodiment of the present invention, a deep learning-based remote sensing data analysis system for soil erosion is also provided. The system includes a data collection and extraction module 1, a data fusion and feature extraction module 2, a feature evaluation and weight calculation module 3, a model building module 4, and a prediction and prevention strategy formulation module 5.

[0157] Data collection and extraction module 1 is used to collect telemetry data of the soil erosion monitoring area and extract soil loss data, surface runoff data and topographic data based on the telemetry data;

[0158] The data fusion and feature extraction module 2 is used to fuse soil loss data, surface runoff data and topographic data, and obtain soil and water loss risk index data and soil erosion rate data through feature extraction technology.

[0159] Feature evaluation and weight calculation module 3 is used to calculate the weight values ​​of soil erosion risk index data and soil erosion rate data using feature evaluation technology;

[0160] Model building module 4 is used to build a soil erosion prediction model based on soil erosion risk index data, soil erosion rate data and weight values, using a deep learning framework and spatiotemporal analysis algorithm.

[0161] Module 5, which is used to predict the state of soil and water loss using a soil and water loss prediction model, and to formulate soil and water loss prevention and control measures based on the prediction results.

[0162] In summary, by utilizing the technical solutions described above in this invention, key information on soil erosion can be rapidly obtained through telemetry data collection and feature extraction. This not only simplifies the data processing workflow but also ensures the accuracy and reliability of the data, laying a solid foundation for subsequent analysis and prediction. The constructed soil erosion prediction model possesses strong learning and generalization capabilities and fully considers temporal and spatial variations, thereby achieving accurate predictions of soil erosion conditions. This provides strong technical support for developing targeted prevention and control measures, contributing to a more effective response to soil erosion problems. Soil erosion prevention and control measures developed based on the prediction results can be precisely tailored to the characteristics of soil erosion in different regions and time periods. This not only effectively curbs the worsening trend of soil erosion but also promotes the protection and restoration of the ecological environment, achieving sustainable development.

[0163] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing remote sensing data of soil erosion based on deep learning, characterized in that, The method includes the following steps: S1. Collect telemetry data of the soil erosion monitoring area, and extract soil loss data, surface runoff data and topographic data based on the telemetry data; S2. Soil loss data, surface runoff data and topographic data are integrated, and water and soil loss risk index data and soil erosion rate data are obtained through feature extraction technology. S3. Calculate the weight values ​​of the soil erosion risk index data and soil erosion rate data using feature evaluation technology; S4. Based on the soil erosion risk index data, soil erosion rate data and weight values, a soil erosion prediction model is constructed using a deep learning framework and spatiotemporal analysis algorithm. S5. Use soil and water loss prediction models to predict the state of soil and water loss, and formulate soil and water loss prevention and control measures based on the prediction results. S2 includes: S21. Using principal component analysis, soil loss data, surface runoff data, and topographic data are integrated into a dataset. S22. Extract soil erosion features from the dataset, including average soil loss, average surface runoff, and the fluctuation of soil loss over different time periods. S23. Calculate the soil and water loss risk index based on the average soil loss and average surface runoff over different time periods, combined with topographic data. S24. Calculate soil erosion rate data based on the fluctuation of soil loss over different time periods and in conjunction with topographic data.

2. The method for analyzing soil erosion telemetry data based on deep learning according to claim 1, characterized in that, The process of collecting telemetry data from the soil erosion monitoring area and extracting soil loss data, surface runoff data, and topographic data based on the telemetry data includes the following steps: S11. Obtain telemetry data of the soil erosion monitoring area through a satellite remote sensing platform; S12. Using the geographic reference points and geometric registration technology of the geographic information system, the telemetry data is geographically corrected, and atmospheric correction software is applied to process the geographically corrected telemetry data. S13. Based on the processed telemetry data, compare the changes in vegetation cover and bare surface area in different time series, and obtain soil loss data in the monitoring area through the differential vegetation index algorithm. S14. Extract water body information from the processed telemetry data, monitor the change in surface water area, and combine rainfall data with hydrological models to obtain surface runoff data for the monitored area. S15. Use remote sensing technology to generate digital elevation model data, and combine it with terrain analysis algorithms to calculate and extract terrain data of the monitored area.

3. The method for analyzing soil erosion telemetry data based on deep learning according to claim 2, characterized in that, The process of using geographic reference points and geometric registration technology of a geographic information system to perform geographic correction on telemetry data, and then applying atmospheric correction software to process the geographically corrected telemetry data includes the following steps: S121. Import the acquired telemetry data into the geographic information system, and select the map projection and coordinates according to the geographic characteristics of the monitoring area; S122. Identify and mark ground control points with known coordinates as geographic reference points in the geographic information system, and select the pixel positions corresponding to the geographic reference points in the telemetry data to establish control point pairs. S123. Perform affine transformation based on control point pairs to perform geographic correction on telemetry data, and then resample the geographically corrected telemetry data. S124. Use atmospheric correction software to perform radiometric calibration on the resampled remote sensing data, converting the values ​​of each band in the remote sensing data into atmospheric radiance values. S125. Use atmospheric correction software to process the radiance value and restore it to the surface reflectance value.

4. The method for analyzing soil erosion telemetry data based on deep learning according to claim 2, characterized in that, The process of comparing changes in vegetation cover and bare surface area across different time series based on processed telemetry data, and obtaining soil loss data for the monitored area using the differential vegetation index algorithm, includes the following steps: S131. Based on the processed telemetry data, the telemetry data for each period are calculated using the normalized vegetation index formula. S132. Set a threshold based on the calculated normalized vegetation index, distinguish between vegetation-covered areas and bare surface areas using the threshold, and vectorize the vegetation-covered areas and bare surface areas into planar layers. S133. In the geographic information system, vegetation cover layers and bare surface layers from different periods are overlaid to identify areas of change and to calculate the changes in vegetation cover area and bare surface area in each period. S134. Based on statistical changes, analyze the changing trends of vegetation cover and bare surface area; S135. Select one of the telemetry data from different periods as the baseline data, and use the differential vegetation index algorithm to calculate the differential vegetation index between the baseline data and the telemetry data of each period. S136. Using a pre-constructed soil erosion assessment model and combined with the differential vegetation index, estimate the soil loss data in the monitoring area. S137. Integrate the estimated soil loss data and trends to obtain soil loss data for the monitoring area at different times.

5. The method for analyzing soil erosion telemetry data based on deep learning according to claim 4, characterized in that, The formula for the normalized vegetation index is: In the formula, NDVI represents the normalized vegetation index; NIR represents near-infrared reflectance; RED indicates the reflectivity in the red light band.

6. The method for analyzing soil erosion telemetry data based on deep learning according to claim 1, characterized in that, The calculation of weight values ​​for soil erosion risk index data and soil erosion rate data using feature evaluation technology includes the following steps: S31. Extract feature data from soil and water loss risk index data and soil erosion rate data, and combine the extracted feature data into a feature matrix; S32. Based on the analysis requirements, define the soil erosion risk index and soil erosion rate as target variables, and use statistical algorithms to calculate the correlation between each feature and the target variables to obtain the feature correlation coefficient. S33. Normalize the feature correlation coefficients to a preset interval, and assign corresponding weight values ​​to each feature based on the normalized feature correlation coefficient scores.

7. The method for analyzing soil erosion telemetry data based on deep learning according to claim 1, characterized in that, The construction of a soil erosion prediction model based on soil erosion risk index data, soil erosion rate data, and weight values, using a deep learning framework and spatiotemporal analysis algorithm, includes the following steps: S41. Use a deep learning framework to perform time series analysis on the soil erosion risk index data and soil erosion rate data to detect whether there is periodic data in the data. If there is periodic data, apply a time series decomposition algorithm to remove the periodic data. If there is no periodic data, continue to analyze the periodic changes in the data. S42. Based on the soil erosion risk index data and soil erosion rate data after removing periodic data, the amplitude and phase information of each frequency component are extracted as feature vectors using beamforming technology in spatiotemporal analysis algorithms. S43. Based on the obtained feature vectors, evaluate the significance of each frequency component through statistical testing, and screen based on the evaluation results to construct a periodic prediction model. S44. The remaining data after removing periodic data is used as random fluctuation data, and a fluctuation prediction model is built using a deep learning framework. S45. Integrate and superimpose the periodic prediction model, the fluctuation prediction model, and the weight values ​​to obtain the soil and water loss prediction model.

8. The method for analyzing soil erosion telemetry data based on deep learning according to claim 7, characterized in that, The formula for the soil erosion prediction model is: Where, This represents the predicted value of soil erosion; y1 represents the predicted value of the periodic prediction model; ω1 represents the weight values ​​of the periodic prediction model; y2 represents the predicted value from the volatility prediction model; ω2 represents the weight value of the fluctuation prediction model.

9. A deep learning-based remote sensing data analysis system for soil erosion, used to implement the deep learning-based remote sensing data analysis method for soil erosion as described in any one of claims 1-8, characterized in that, The system includes a data collection and extraction module, a data fusion and feature extraction module, a feature evaluation and weight calculation module, a model building module, and a prediction and prevention strategy formulation module; The data collection and extraction module is used to collect telemetry data of the soil erosion monitoring area and extract soil loss data, surface runoff data and topographic data based on the telemetry data. The data fusion and feature extraction module is used to fuse soil loss data, surface runoff data and topographic data, and obtain soil and water loss risk index data and soil erosion rate data through feature extraction technology. The feature evaluation and weight calculation module is used to calculate the weight values ​​of the soil erosion risk index data and the soil erosion rate data using feature evaluation technology. The model building module is used to build a soil erosion prediction model based on soil erosion risk index data, soil erosion rate data and weight values, using a deep learning framework and spatiotemporal analysis algorithm. The prediction and prevention strategy formulation module is used to predict the soil erosion situation using a soil erosion prediction model and formulate soil erosion prevention and control measures based on the prediction results. The data obtained by integrating soil loss data, surface runoff data, and topographic data, and using feature extraction techniques to obtain soil erosion risk index data and soil erosion rate data, include: Principal component analysis was used to integrate soil loss data, surface runoff data, and topographic data into a dataset. Features of soil erosion are extracted from the dataset, including average soil loss, average surface runoff, and the volatility of soil loss over different time periods. Based on the average soil loss and average surface runoff over different time periods, and combined with topographic data, soil erosion risk index data are calculated. Based on the volatility of soil loss over different time periods, and combined with topographic data, soil erosion rate data are calculated.

Images