Method and system for dynamically monitoring erosion of soft sandstone slope

Abstract

The invention discloses a method and system for dynamically monitoring soft sandstone slope erosion, and relates to the technical field of geological disaster monitoring, and the method comprises the steps: building a monitoring sample area on a soft sandstone slope, obtaining initial terrain point cloud data, generating a reference digital elevation model, and recognizing a vegetation space pattern; collecting rainfall data in real time, and triggering an erosion monitoring program when rainfall parameters meet a preset triggering condition; acquiring real-time terrain point cloud data, generating a current digital elevation model and acquiring a slope fracture feature set; based on the elevation variation of the current digital elevation model and the reference digital elevation model, carrying out erosion intensity partitioning; determining an erosion regulation effect coefficient of each vegetation coverage subarea by combining vegetation spatial pattern data; and establishing an erosion risk assessment model, and outputting erosion sensitivity grading of the monitoring sample area and a key protection area identification result. According to the invention, automatic and high-precision dynamic monitoring of soft sandstone slope erosion is realized, and a scientific basis is provided for water and soil conservation and geological disaster prevention and control.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, in particular to a method and system for dynamically monitoring erosion of sandstone slopes. Background Art

[0002] As a typical soft rock, arsenic sandstone is widely distributed across the Loess Plateau in northwest China. It exhibits weak erosion resistance and is easily weathered and broken. Under external forces such as rainfall, arsenic sandstone slopes are highly susceptible to soil erosion, severely impacting the local ecological environment and engineering construction. Therefore, effective monitoring and early warning of erosion on arsenic sandstone slopes are crucial.

[0003] Currently, slope erosion monitoring methods primarily include traditional ground surveying and near-surface laser scanning. Traditional ground surveying involves installing monitoring devices such as erosion probes and stakes on the slope surface, and conducting regular manual measurements to obtain data on topographic changes. Near-surface laser scanning uses a laser scanner to obtain high-precision three-dimensional point cloud data of the slope surface. This data is then compared over time to analyze topographic changes. While these methods have been used to some extent in slope erosion monitoring, they still have limitations in terms of real-time response to rainfall events and quantification of the effects of vegetation regulation. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a dynamic monitoring method and system for arsenic sandstone slope erosion, which is used to solve the technical problem that existing monitoring methods are difficult to achieve real-time monitoring of slope erosion driven by rainfall and multi-factor collaborative analysis.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for dynamic monitoring of erosion on sandstone slopes, comprising: A monitoring plot was established on the arsenic sandstone slope, and a 3D laser scanner was used to obtain initial terrain point cloud data, generate a baseline digital elevation model, and identify the spatial pattern of vegetation. Set up a meteorological monitoring station to collect rainfall data in real time. When the rainfall parameters meet the preset trigger conditions, the erosion monitoring program is automatically triggered. Acquire real-time terrain point cloud data, generate the current digital elevation model, and obtain the slope crack feature set through point cloud roughness analysis; Based on the elevation change between the current digital elevation model and the benchmark digital elevation model, the monitoring area is divided into erosion intensity zones; Based on the erosion intensity zoning results, spatial overlay analysis was conducted in combination with vegetation spatial pattern data to determine the erosion control effect coefficient of each vegetation coverage zone. Based on the erosion control effect coefficient and the slope crack feature set, an erosion risk assessment model is established to output the erosion sensitivity classification of the monitoring sample area and the identification results of key protection areas, and generate a dynamic monitoring report.

[0007] As a preferred embodiment of the method for dynamically monitoring erosion of sandstone slopes of the present invention, generating a reference digital elevation model and identifying the spatial pattern of vegetation includes: A monitoring sample area was set up on the arsenic sandstone slope, and the coordinates of the sample area boundary were determined using positioning technology to establish a reference coordinate system; A ground-based three-dimensional laser scanner was used to scan the monitoring sample area to obtain initial terrain point cloud data; Preprocess and rasterize the initial terrain point cloud data to generate a benchmark digital elevation model, and calculate the terrain factors based on the benchmark digital elevation model; Feature extraction is performed on multispectral remote sensing image data. Combined with the initial terrain point cloud data, a fusion classification algorithm is used to identify vegetation areas and bare soil areas to obtain vegetation spatial distribution data. Calculate vegetation coverage based on vegetation spatial distribution data and perform multi-level zoning based on vegetation coverage; Based on vegetation spatial distribution data and topographic factors, the landscape pattern analysis method was used to calculate the landscape pattern index of each vegetation coverage zone to form vegetation spatial pattern data.

[0008] As a preferred solution of the dynamic monitoring method for arsenic sandstone slope erosion of the present invention, the preset trigger conditions include the rainfall intensity reaching a preset trigger intensity threshold or the cumulative rainfall within a preset time window reaching a preset trigger amount threshold.

[0009] As a preferred solution of the method for dynamic monitoring of arsenic sandstone slope erosion of the present invention, generating a current digital elevation model and obtaining a slope crack feature set through point cloud roughness analysis includes: After receiving the trigger signal from the erosion monitoring program, the scanning interval is determined according to the current rainfall intensity; Control the ground 3D laser scanner according to the scanning interval time, perform time-series scanning on the monitoring sample area, and obtain real-time terrain point cloud data; Perform spatial registration and rasterization processing on real-time terrain point cloud data to generate the current digital elevation model; Surface roughness index is extracted based on the current digital elevation model, and slope crack areas are identified through multi-scale roughness analysis; The slope crack area is vectorized, and spatial analysis is performed based on the vectorization results to determine the crack density, crack connectivity and crack direction, and generate a slope crack feature set.

[0010] As a preferred embodiment of the method for dynamically monitoring erosion of sandstone slopes of the present invention, the erosion intensity zoning of the monitoring sample area includes: Calculate the point-by-point difference between the current digital elevation model and the benchmark digital elevation model to obtain the distribution of elevation changes; Determine the erosion intensity classification threshold based on the statistical characteristics of the elevation change distribution; The monitoring sample area is spatially partitioned according to the erosion intensity classification threshold, and the partition boundaries are optimized in combination with terrain factors to form erosion intensity partitions.

[0011] As a preferred embodiment of the method for dynamic monitoring of erosion of sandstone slopes of the present invention, the spatial overlay analysis combined with vegetation spatial pattern data includes: The layer overlay algorithm is used to spatially overlay the vegetation spatial pattern data and the erosion intensity zoning results to generate a vegetation-erosion composite layer; Based on the vegetation-erosion composite layer, the area distribution of different erosion intensity levels in each vegetation coverage zone was counted, and the erosion intensity index of each vegetation coverage zone was calculated; Taking the erosion intensity index of the bare soil area as a benchmark reference, the erosion control effect coefficient of each vegetation coverage zone was calculated; Based on the landscape pattern index of each vegetation coverage zone, the erosion control effect coefficient was corrected using the weighted correction method to obtain the corrected erosion control effect coefficient. Establish a correspondence table between vegetation coverage zones and modified erosion control effect coefficients.

[0012] As a preferred solution of the method for dynamic monitoring of erosion of sandstone slopes of the present invention, a multi-factor erosion risk evaluation index system is constructed based on the erosion control effect coefficient and the slope crack feature set; The weighted superposition algorithm with adaptive weight optimization is used to calculate the multi-factor erosion risk assessment index system and generate a comprehensive erosion risk index distribution map; Based on the comprehensive erosion risk index distribution map, the fuzzy C-means clustering algorithm was used to divide the monitoring area into multiple erosion sensitivity levels to generate erosion sensitivity classification results. For the high sensitivity level and medium-high sensitivity level in the erosion sensitivity classification results, a fracture network topology structure is constructed based on the fracture feature set, and a graph theory algorithm is used to calculate the risk propagation path and identify key protection areas; Key protection areas are prioritized based on a multi-criteria approach, based on comprehensive erosion risk index and risk transmission potential, and a dynamic monitoring report is generated.

[0013] In a second aspect, the present invention provides a dynamic monitoring system for arsenic sandstone slope erosion, comprising: The initial terrain acquisition module is used to establish a monitoring sample area on the arsenic sandstone slope, use a 3D laser scanner to obtain initial terrain point cloud data, generate a baseline digital elevation model, and identify the spatial pattern of vegetation; The meteorological monitoring module is used to set up a meteorological monitoring station to collect rainfall data in real time. When the rainfall parameters meet the preset trigger conditions, the erosion monitoring program is automatically triggered; The terrain change monitoring module is used to obtain real-time terrain point cloud data, generate the current digital elevation model, and obtain the slope crack feature set through point cloud roughness analysis; The erosion intensity zoning module is used to zonate the erosion intensity of the monitoring area based on the elevation change between the current digital elevation model and the benchmark digital elevation model; The erosion control analysis module is used to perform spatial overlay analysis based on the erosion intensity zoning results and combined with vegetation spatial pattern data to determine the erosion control effect coefficient of each vegetation coverage zone; The risk assessment module is used to establish an erosion risk assessment model based on the erosion control effect coefficient and the slope crack feature set, output the erosion sensitivity classification of the monitoring sample area and the identification results of key protection areas, and generate a dynamic monitoring report.

[0014] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for dynamic monitoring of arsenic sandstone slope erosion as in the first aspect of the present invention is implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for dynamic monitoring of arsenic sandstone slope erosion according to the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: by establishing an automatic monitoring mechanism triggered by rainfall, combined with real-time meteorological data collection and dynamic scanning frequency adjustment, accurate capture of key erosion periods is achieved, thereby improving the pertinence and timeliness of monitoring work. By integrating three-dimensional laser scanning, multispectral remote sensing imaging and point cloud roughness analysis technology, coordinated monitoring of terrain changes, vegetation patterns and crack characteristics is achieved, and the monitoring accuracy and data reliability in the complex environment of arsenic sandstone slopes are improved. By constructing a quantitative model of vegetation erosion regulation effects and a multi-factor risk assessment system, a full-process technical chain from data collection to risk warning is realized, which improves the scientific nature of erosion sensitivity identification and the accuracy of key protection area delineation, and provides systematic technical support for soil and water conservation decision-making in arsenic sandstone areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a flow chart of the dynamic monitoring method for erosion of arsenic sandstone slope.

[0019] Figure 2 Flowchart for obtaining slope crack feature set for dynamic monitoring method of arsenic sandstone slope erosion.

[0020] Figure 3 A flow chart was constructed for the erosion risk assessment model of the dynamic monitoring method for erosion on arsenic sandstone slopes.

[0021] Figure 4 This is a module diagram of the dynamic monitoring system for erosion of arsenic sandstone slopes. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0025] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides a method for dynamic monitoring of erosion of arsenic sandstone slope, the flow chart is as follows Figure 1 As shown, the following steps are included: S1: A monitoring sample area was established on the arsenic sandstone slope. A 3D laser scanner was used to obtain initial terrain point cloud data, generate a benchmark digital elevation model, and identify the spatial pattern of vegetation.

[0026] Specifically, step S1 includes: S1.1: Set up a monitoring sampling area on the arsenic sandstone slope, use positioning technology to determine the boundary coordinates of the sampling area, and establish a reference coordinate system.

[0027] In one embodiment, a monitoring plot was established on a slope with typical arsenic sandstone geology. The plot should include areas with varying vegetation coverage to analyze the effect of vegetation on erosion. The monitoring plot typically ranges from 100 to 500 square meters. RTK-GPS equipment was used to measure boundary coordinates and set control points to establish a unified reference coordinate system.

[0028] S1.2: Use a ground-based 3D laser scanner to scan the monitoring sample area and obtain initial terrain point cloud data.

[0029] The initial terrain point cloud data includes three-dimensional coordinate information and reflection intensity information.

[0030] S1.3: Preprocess and rasterize the initial terrain point cloud data to generate a benchmark digital elevation model, and calculate terrain factors based on the benchmark digital elevation model.

[0031] Preprocessing includes outlier removal and noise filtering, and terrain factors include slope, aspect, and surface roughness. Specifically, slope is calculated using the maximum slope algorithm, aspect is calculated using the gradient direction method, and surface roughness is obtained by calculating the standard deviation of elevation within the grid window.

[0032] S1.4: Extract features from multispectral remote sensing image data, combine them with initial terrain point cloud data, and use a fusion classification algorithm to identify vegetation areas and bare soil areas to obtain vegetation spatial distribution data.

[0033] In one embodiment, multispectral remote sensing image data of the monitoring area can be obtained using drone multispectral imaging or high-resolution satellite remote sensing data. Based on monitoring needs, vegetation spatial distribution data can be updated regularly. The update cycle is determined by vegetation growth characteristics, monitoring accuracy requirements, and rainfall event frequency, typically monthly, with event-triggered updates following heavy rainfall events.

[0034] Specifically, the multispectral remote sensing image data is preprocessed with radiation correction and geometric correction to eliminate the influence of atmospheric scattering and geometric distortion, and obtain standardized multispectral image data. Based on the standardized multispectral image data, the normalized vegetation index, enhanced vegetation index and soil-adjusted vegetation index are calculated to form a multidimensional vegetation index feature vector. Among them, the normalized vegetation index is obtained by the standardized difference ratio of the near-infrared and red light bands, the enhanced vegetation index is obtained by a multi-band optimization algorithm and the introduction of an atmospheric correction factor, and the soil-adjusted vegetation index is obtained by eliminating background interference through a soil brightness correction factor. Taking into account the characteristics of the arsenic sandstone area with a lot of exposed soil and low vegetation coverage, the combination of the normalized vegetation index, the enhanced vegetation index and the soil-adjusted vegetation index can cover the full range of identification needs from bare soil to high-coverage vegetation.

[0035] Furthermore, the initial terrain point cloud data was projected into a multispectral image coordinate system, and spatial registration and fusion of the point cloud data and the multispectral image data were achieved using the nearest neighbor interpolation method. Reflectance intensity information was extracted from the point cloud data and combined with the multidimensional vegetation index feature vector to construct a spectral-intensity fusion feature set. Based on the spectral-intensity fusion feature set, a support vector machine classification algorithm was used to perform pixel-level classification of the monitoring sample area to identify vegetation areas and bare soil areas. The classification results were subjected to morphological filtering and connectivity analysis to remove isolated noise pixels and smooth regional boundaries to generate vegetation spatial distribution data.

[0036] S1.5: Calculate vegetation coverage based on vegetation spatial distribution data and perform multi-level zoning based on vegetation coverage.

[0037] It should be noted that the multi-level zoning includes bare soil, low cover, medium cover, and high cover. Taking into account the particularity of the arsenic sandstone area, the zoning is based on vegetation cover thresholds: bare soil areas have a vegetation cover of less than 3%, low cover areas have a vegetation cover of 3%-20%, medium cover areas have a vegetation cover of 20%-40%, and high cover areas have a vegetation cover of more than 40%.

[0038] S1.6: Based on vegetation spatial distribution data and topographic factors, use landscape pattern analysis methods to calculate the landscape pattern index of each vegetation coverage zone to form vegetation spatial pattern data.

[0039] Preferably, the landscape pattern index includes patch density, connectivity index and landscape shape index.

[0040] Specifically, the calculation process of the landscape pattern index is as follows: First, based on the vegetation coverage zoning results, patches are identified, and the eight-neighborhood connectivity algorithm is used to aggregate adjacent pixels of the same coverage type into independent patches, and the number and area distribution of patches in each partition are counted. Secondly, the patch density index is calculated by the ratio of the total number of patches in each vegetation coverage partition to the total area of the partition. Thirdly, the connectivity index is calculated, and the fixed buffer distance method is used to measure the spatial distance between similar vegetation patches. The spatial connectivity between vegetation patches is evaluated by the proportion of buffer overlap area. Finally, the landscape shape index is calculated. The circularity index is calculated based on the ratio of the perimeter to the area of each patch and is standardized to evaluate the shape complexity and edge effect of the vegetation patches. Combined with the slope and aspect information in the terrain factors, each landscape pattern index is subjected to terrain-weighted correction to generate comprehensive vegetation spatial pattern data.

[0041] It should be noted that patch density, connectivity index, and landscape shape index were selected because they can effectively reflect the regulatory effect of vegetation spatial distribution on erosion processes. Specifically, patch density reflects the degree of continuity in vegetation distribution. Vegetation cover with greater continuity provides a more stable overall protective effect. The connectivity index reflects the spatial connectivity between vegetation patches. The better the connectivity, the stronger the overall soil and water conservation effect of the vegetation. The landscape shape index reflects the regularity of vegetation patches. Vegetation patches with greater regularity have greater cover stability and can effectively reduce the adverse effects of edge effects on erosion resistance.

[0042] S2: Set up a meteorological monitoring station to collect rainfall data in real time. When the rainfall parameters meet the preset trigger conditions, the erosion monitoring program is automatically triggered.

[0043] The preset trigger conditions include rainfall intensity reaching a preset trigger intensity threshold or cumulative rainfall reaching a preset trigger volume threshold within a preset time window (e.g., 24 hours). The preset trigger intensity threshold is a critical rainfall intensity value determined based on local historical rainfall data and statistical analysis of erosion events. The preset trigger volume threshold is a critical cumulative rainfall value within a preset time window, determined based on regional climate characteristics and topographic conditions.

[0044] It is important to note that the dual triggering conditions of rainfall intensity and accumulated rainfall correspond to different stages of the erosion process, based on the physical mechanism of erosion on sandstone slopes. Rainfall intensity determines the initiation of soil stripping, while accumulated rainfall determines the formation of surface runoff. This dual triggering mechanism, compared to a single parameter trigger, can more accurately identify true erosion events, reduce false triggers caused by the complexity of rainfall characteristics, improve monitoring accuracy and reliability, and achieve a transition from periodic monitoring to precise event-driven monitoring.

[0045] S3: After the erosion monitoring program is triggered, a ground-based 3D laser scanner is used to scan the monitoring area to obtain real-time terrain point cloud data, generate the current digital elevation model, and obtain the slope crack feature set through point cloud roughness analysis.

[0046] Specifically, the flow chart for obtaining the slope crack feature set is as follows: Figure 2 As shown, including: S3.1: After receiving the trigger signal from the erosion monitoring program, determine the scanning interval time according to the current rainfall intensity.

[0047] It should be noted that the scanning interval is set according to the rainfall intensity level. The greater the rainfall intensity, the shorter the scanning interval. This adaptive scanning strategy not only ensures the data acquisition density during the critical period, but also avoids unnecessary equipment loss and data redundancy.

[0048] S3.2: Control the ground 3D laser scanner according to the scanning interval time, perform time-series scanning on the monitoring sample area, and obtain real-time terrain point cloud data.

[0049] S3.3: Perform spatial registration and rasterization on the real-time terrain point cloud data to generate the current digital elevation model.

[0050] Specifically, the real-time terrain point cloud data is roughly preprocessed, and outliers with obvious anomalies are identified and removed based on distance thresholds and density statistics. Stable feature points are extracted from the baseline digital elevation model as control points. A feature descriptor matching algorithm is used to identify corresponding control points in the preprocessed real-time terrain point cloud data. An iterative closest point algorithm is used to accurately align the control points, and a 3D rigid body transformation matrix is calculated to unify the real-time terrain point cloud data into the baseline coordinate system.

[0051] The registered point cloud data is then fine-tuned for environmental interference, using a density clustering algorithm to identify and remove anomalous scattering points and noise. A statistical filtering algorithm is used for noise filtering, and adaptive compensation is performed on the point cloud reflection intensity based on rainfall intensity and laser wavelength characteristics. The corrected point cloud data is rasterized using a natural neighbor interpolation algorithm to generate a current digital elevation model that matches the resolution and spatial extent of the baseline digital elevation model.

[0052] Through the above-mentioned phased processing strategy, the data interference in the rainfall environment is effectively removed while ensuring the registration accuracy, and the generation quality and reliability of the current digital elevation model are improved.

[0053] S3.4: Extract the surface roughness index based on the current digital elevation model and identify the slope crack area through multi-scale roughness analysis.

[0054] Specifically, the terrain relief of each grid cell is calculated based on the current digital elevation model to generate an initial surface roughness index distribution map. Using a moving window technique, multiple calculation windows of different sizes are set up, and the surface roughness index at each window scale is calculated to form a multi-scale roughness dataset. A differential analysis is performed on the multi-scale roughness dataset to identify areas with abnormal roughness as potential crack candidates. Combining terrain convexity analysis with slope aspect information, potential crack candidate areas are screened and merged based on an adaptive roughness threshold and spatial connectivity constraints to extract slope crack areas.

[0055] Optionally, the extracted slope crack areas are optimized using morphological filtering and geometric feature constraints to determine the final slope crack area distribution.

[0056] It is noteworthy that traditional fracture identification methods rely primarily on manual visual interpretation or image processing techniques, making it difficult to accurately identify the fine fracture structure of arsenic sandstone slopes. This invention utilizes multi-scale roughness analysis to automatically identify fracture features at different scales. Combined with terrain concavity and convexity analysis and spatial connectivity constraints, it improves the accuracy and reliability of fracture identification on arsenic sandstone slopes. This technology provides key terrain structural parameters for subsequent erosion risk assessment and forms the technical foundation for erosion monitoring.

[0057] S3.5: Vectorize the slope crack area, perform spatial analysis based on the vectorization results to determine the crack density, crack connectivity, and crack direction, and generate a slope crack feature set.

[0058] Furthermore, a skeleton extraction algorithm was used to vectorize the slope crack region and extract its centerline structure. Based on the vectorized crack geometry, a spatial density analysis method was used to calculate the crack area ratio and number of cracks per unit area to determine the crack density. Spatial topology analysis was used to assess the spatial connectivity between crack centerlines and calculate the crack connectivity index. A crack centerline direction statistics method was used to extract the primary extension direction of the crack region and determine the crack strike distribution. Based on these spatial analysis results, a slope crack feature set was established.

[0059] S4: Based on the elevation change between the current digital elevation model and the benchmark digital elevation model, the monitoring area is divided into erosion intensity zones.

[0060] Specifically, step S4 includes: S4.1: Calculate the point-by-point difference between the current digital elevation model and the benchmark digital elevation model to obtain the distribution of elevation changes.

[0061] S4.2: Determine the erosion intensity classification threshold based on the statistical characteristics of the elevation change distribution.

[0062] In one embodiment, the process of determining the erosion intensity classification threshold includes: calculating the statistical parameters of the elevation change distribution, including the mean, standard deviation and skewness. The initial classification threshold is set using the standard deviation classification method, with the mean as the center, and the skewness is calculated. 、 、 A multi-level classification system is established using intervals of multiples of the standard deviation. The absolute value of the skewness is determined to be greater than 0.5. If so, quantile analysis is used to adjust the classification thresholds for the extreme value intervals. The 5th and 95th percentile values are calculated to optimize the boundaries of the initial classification thresholds. The classification thresholds are verified and adjusted, and modified according to specific application scenarios, with the adjustment range not exceeding 15% of the original threshold. The final erosion intensity classification threshold system is established.

[0063] S4.3: Spatially partition the monitoring area according to the erosion intensity classification threshold, optimize the partition boundaries based on topographic factors, and form erosion intensity zones.

[0064] Optionally, the erosion intensity zones include a sedimentation zone, a slight erosion zone, a light erosion zone, a moderate erosion zone, and a heavy erosion zone. In other embodiments, other classification methods may be used according to actual needs, such as a three-level zone (light, moderate, heavy) or a seven-level zone.

[0065] Optimizing the partition boundaries based on topographic factors preferably involves comprehensively considering topographic factors such as slope, aspect, and surface roughness, identifying areas with significant changes in topographic characteristics as natural boundaries. Boundaries are smoothed in transitional areas where topographic factors gradually change, and adjacent areas with similar topographic factors within the same erosion intensity level are merged to eliminate fragmented partitions caused by local fluctuations, resulting in clearly defined, spatially continuous erosion intensity partitions.

[0066] Optimally, through elevation change calculation, intelligent threshold determination, and terrain factor optimization, the accuracy and reliability of erosion intensity zoning can be improved. The intelligent grading threshold determination mechanism reduces subjective judgment errors, while terrain factor boundary optimization eliminates unreasonable boundaries caused by statistical grading. The automated process improves work efficiency.

[0067] S5: Based on the erosion intensity zoning results, spatial overlay analysis is performed in combination with vegetation spatial pattern data to determine the erosion control effect coefficient of each vegetation coverage zone.

[0068] It should be noted that the spatial overlay analysis based on vegetation spatial pattern data and erosion intensity zoning results is an important innovation of the present invention. Traditional erosion assessment methods often consider vegetation as a single factor, ignoring the impact of vegetation spatial configuration on erosion control. The present invention uses a layer overlay algorithm to achieve accurate matching of vegetation spatial pattern and erosion intensity zoning, quantitatively determines the erosion control effect coefficient of each vegetation coverage zone, and corrects it in combination with the landscape pattern index, revealing the regulatory mechanism of vegetation spatial configuration on slope erosion. This method provides key vegetation control parameters for the erosion risk assessment model, realizing the transition from a single vegetation factor to vegetation-erosion coupling analysis.

[0069] Specifically, step S5 includes: S5.1: Use a layer overlay algorithm to spatially overlay the vegetation spatial pattern data and the erosion intensity zoning results to generate a vegetation-erosion composite layer.

[0070] Preferably, the vegetation spatial pattern data and erosion intensity zoning results are geometrically corrected and projected uniformly before layer overlay. A raster data joint analysis method is used to generate composite attribute values by combining pixel-level attributes, and to optimize the fragmentation of the overlay results.

[0071] S5.2: Based on the vegetation-erosion composite layer, calculate the area distribution of different erosion intensity levels within each vegetation coverage zone and calculate the erosion intensity index for each vegetation coverage zone.

[0072] In this embodiment, the erosion intensity index is calculated using the weighted area method, which is determined based on the area proportion of each erosion intensity level and the corresponding weight coefficient, and the weight coefficient is set in increasing order according to the erosion intensity level.

[0073] S5.3: Using the erosion intensity index of the bare soil area as a benchmark reference, calculate the erosion control effect coefficient for each vegetation coverage zone.

[0074] The erosion control effect coefficient is determined using a benchmark comparison method, quantifying the strength of the control effect of each vegetation coverage zone relative to the bare soil zone benchmark. If there are no significant bare soil areas within the study area, the zone with the lowest vegetation coverage can be selected as the benchmark reference.

[0075] S5.4: Based on the landscape pattern index of each vegetation coverage zone, the erosion control effect coefficient is corrected using a weighted correction method to obtain a corrected erosion control effect coefficient.

[0076] Specifically, the entropy weight method was used to determine the weight coefficients for patch density, connectivity index, and landscape shape index. After normalization, the weighted summation was performed to obtain the comprehensive landscape pattern correction coefficient. This comprehensive landscape pattern correction coefficient was then multiplied by the original erosion control effect coefficient to obtain the modified erosion control effect coefficient. By correcting the landscape pattern characteristics, the spatial accuracy and ecological rationality of the erosion control effect coefficient were improved.

[0077] S5.5: Establish a correspondence table between vegetation coverage zones and modified erosion control effect coefficients.

[0078] Optimally, vegetation-erosion spatial overlay analysis, quantification of erosion control effects, and landscape pattern correction enable an accurate assessment of vegetation's regulatory effect on soil erosion. A layer overlay algorithm accurately matches vegetation spatial patterns with erosion intensity zones, improving the statistical accuracy of erosion intensity distribution characteristics within each vegetation coverage zone. The erosion control effect coefficient is calculated using bare soil areas as a benchmark, and weighted correction is performed in conjunction with the landscape pattern index. This accurately quantifies the net erosion protection effect of vegetation, considers the impact of vegetation spatial configuration patterns on erosion control effects, and improves the ecological rationality and spatial representativeness of the assessment results.

[0079] S6: Based on the erosion control effect coefficient and slope crack feature set, an erosion risk assessment model is established to output the erosion sensitivity classification of the monitoring sample area and the identification results of key protection areas, and generate a dynamic monitoring report.

[0080] Among them, the core innovation of the present invention is to establish an erosion risk assessment model based on the erosion control effect coefficient and the slope crack feature set. Traditional erosion risk assessments are mostly based on a single factor or a simple empirical formula, which is difficult to fully reflect the complexity of arsenic sandstone slope erosion. The present invention organically combines the vegetation control effect and terrain structure characteristics. The erosion control effect coefficient integrates the coupling relationship between the erosion intensity zoning and the vegetation spatial pattern, reflecting the differentiated control ability of vegetation on areas with different erosion degrees; the slope crack feature set reflects the control effect of geological structure on erosion sensitivity.

[0081] On this basis, adaptive weight optimization and fuzzy clustering techniques were used to quantitatively express the interaction between vegetation regulation and terrain structural characteristics, and a multi-factor integrated erosion risk assessment model was constructed. This model accurately classifies erosion susceptibility and intelligently identifies key protection areas, completing the transition from single-factor static assessment to multi-factor dynamic comprehensive assessment, and achieving a shift from post-event analysis to pre-event warning, providing scientific decision-making support for erosion protection on Arsenic sandstone slopes.

[0082] Specifically, the flow chart for constructing the erosion risk assessment model is as follows: Figure 3 As shown, including: S6.1: Construct a multi-factor erosion risk assessment index system based on the erosion control effect coefficient and slope crack feature set.

[0083] Optionally, the erosion control effect coefficient is converted into a vegetation control risk index, the fracture density, fracture connectivity and fracture direction are converted into geological structure risk indexes, each risk index is standardized, and the standardized risk indexes are spatially aligned and classified and organized according to vegetation control and geological structure categories.

[0084] S6.2: Use the weighted superposition algorithm with adaptive weight optimization to calculate the multi-factor erosion risk assessment index system and generate a comprehensive erosion risk index distribution map.

[0085] Adaptive weight optimization dynamically adjusts the weight coefficients of various evaluation indicators based on current rainfall intensity and terrain factors. Specifically, a weight adjustment function is constructed based on current rainfall intensity and terrain factors to dynamically adjust the weight coefficients of vegetation regulation and geological structure indicators. A weighted overlay algorithm is used to spatially overlay the standardized risk indicators to generate a comprehensive erosion risk index distribution map.

[0086] Preferably, the algorithm can dynamically adjust the weights of evaluation indicators according to real-time environmental conditions, making the erosion risk assessment results more in line with actual environmental changes and enhancing the model's adaptability to different seasons and climatic conditions.

[0087] S6.3: Based on the comprehensive erosion risk index distribution map, the fuzzy C-means clustering algorithm is used to divide the monitoring sample area into multiple erosion sensitivity levels to generate erosion sensitivity classification results.

[0088] Furthermore, the risk index values of pixels were extracted from the comprehensive erosion risk index distribution map to construct a sample data set, and the number of clusters was set to correspond to the number of erosion sensitivity levels. The fuzzy C-means clustering algorithm was used for iterative calculation, and each pixel was assigned to the corresponding cluster category according to the maximum membership principle. The clustering results were mapped to erosion sensitivity levels according to the risk index size of the cluster center, and an erosion sensitivity grading result map and a grade division threshold table were generated.

[0089] Optionally, the erosion sensitivity levels include high sensitivity, medium-high sensitivity, medium sensitivity, low sensitivity, and very low sensitivity. This algorithm can effectively handle the fuzzy boundary problem between erosion sensitivity levels, provide smooth transition area division, and avoid the sudden boundary problem caused by rigid classification.

[0090] S6.4: For the high sensitivity level and medium-high sensitivity level in the erosion sensitivity classification results, the fracture network topology structure is constructed based on the fracture feature set, and the graph theory algorithm is used to calculate the risk propagation path and identify key protection areas.

[0091] Preferably, areas with fracture density exceeding a preset density threshold are designated as network nodes. Network edges are established between adjacent areas whose fracture connectivity exceeds a preset connectivity threshold. The weight coefficients of the network edges are calculated based on the fracture orientation to generate a weighted fracture network topology. High-sensitivity and medium-high-sensitivity areas are designated as risk source nodes. A path algorithm is used to calculate the propagation paths from each risk source node to other nodes. The risk propagation intensity is calculated based on the network edge weights and path lengths to determine the dominant direction and impact range of risk propagation. The cumulative risk propagation intensity of each node is calculated, and nodes and their surrounding areas with cumulative propagation intensity exceeding the preset propagation threshold are identified as key protection areas.

[0092] Among them, the preset density threshold is determined by statistically analyzing the crack density distribution characteristics, the preset connectivity threshold is determined by analyzing the crack connectivity distribution law, and the preset transmission threshold is determined by the risk transmission intensity distribution statistics.

[0093] Optimally, by constructing a fracture network topology and employing graph-theoretic algorithms, we can systematically identify the propagation paths and key control nodes of erosion risk, effectively revealing the spatial transmission mechanisms of erosion risk within a region. This approach overcomes the limitation of static sensitivity grading in reflecting the dynamic propagation of risk. By identifying key control nodes for risk propagation, we shift from identifying high-risk areas to selecting optimal protection locations, improving the scientific nature and effectiveness of protective measures deployment.

[0094] S6.5: Prioritize key protection areas based on a multi-criteria approach based on comprehensive erosion risk index and risk transmission potential.

[0095] It should be noted that multi-criteria priority ranking refers to the use of multi-criteria decision-making methods such as hierarchical analysis method or TOPSIS based on the two evaluation criteria of comprehensive erosion risk index and risk transmission potential to prioritize the management and control of key protection areas.

[0096] S6.6: Integrate the erosion sensitivity classification results, key protection areas and priority rankings to generate a dynamic monitoring report.

[0097] Optimally, multi-factor risk assessment, adaptive weight optimization, and sensitivity grading enable accurate identification and dynamic monitoring of erosion risk. By combining a multi-factor evaluation index system with an adaptive weight optimization algorithm, dynamic adjustment of weight coefficients is achieved, improving the accuracy and adaptability of erosion risk assessment. Establishing a multi-criteria prioritization system and generating dynamic monitoring reports enhances the systematic nature of erosion risk management and the scientific nature of decision-making support.

[0098] This embodiment also provides a dynamic monitoring system for arsenic sandstone slope erosion, the module diagram is as follows Figure 4 As shown, including: The initial terrain acquisition module is used to establish a monitoring sample area on the arsenic sandstone slope, use a 3D laser scanner to obtain initial terrain point cloud data, generate a baseline digital elevation model, and identify the spatial pattern of vegetation; The meteorological monitoring module is used to set up a meteorological monitoring station to collect rainfall data in real time. When the rainfall parameters meet the preset trigger conditions, the erosion monitoring program is automatically triggered; The terrain change monitoring module is used to obtain real-time terrain point cloud data, generate the current digital elevation model, and obtain the slope crack feature set through point cloud roughness analysis; The erosion intensity zoning module is used to zonate the erosion intensity of the monitoring area based on the elevation change between the current digital elevation model and the benchmark digital elevation model; The erosion control analysis module is used to perform spatial overlay analysis based on the erosion intensity zoning results and combined with vegetation spatial pattern data to determine the erosion control effect coefficient of each vegetation coverage zone; The risk assessment module is used to establish an erosion risk assessment model based on the erosion control effect coefficient and the slope crack feature set, output the erosion sensitivity classification of the monitoring sample area and the identification results of key protection areas, and generate a dynamic monitoring report.

[0099] This embodiment also provides a computer device suitable for the dynamic monitoring method of arsenic sandstone slope erosion, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the dynamic monitoring method of arsenic sandstone slope erosion proposed in the above embodiment.

[0100] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0101] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for dynamic monitoring of arsenic sandstone slope erosion proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0102] In summary, the present invention achieves accurate capture of key erosion periods by establishing an automatic monitoring mechanism triggered by rainfall, combined with real-time meteorological data collection and dynamic scanning frequency adjustment, thereby improving the pertinence and timeliness of monitoring work. By integrating three-dimensional laser scanning, multispectral remote sensing imaging and point cloud roughness analysis technology, coordinated monitoring of terrain changes, vegetation patterns and crack characteristics is achieved, improving the monitoring accuracy and data reliability in the complex environment of arsenic sandstone slopes. By constructing a quantitative model of vegetation erosion regulation effects and a multi-factor risk assessment system, a full-process technical chain from data collection to risk warning is realized, improving the scientific nature of erosion sensitivity identification and the accuracy of key protection area delineation, and providing systematic technical support for soil and water conservation decision-making in arsenic sandstone areas.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A dynamic monitoring method for erosion of sandstone slopes, characterized by: include: A monitoring plot was established on the arsenic sandstone slope, and a 3D laser scanner was used to obtain initial terrain point cloud data, generate a baseline digital elevation model, and identify the spatial pattern of vegetation. Set up a meteorological monitoring station to collect rainfall data in real time. When the rainfall parameters meet the preset trigger conditions, the erosion monitoring program is automatically triggered. Acquire real-time terrain point cloud data, generate the current digital elevation model, and obtain the slope crack feature set through point cloud roughness analysis; Based on the elevation change between the current digital elevation model and the benchmark digital elevation model, the monitoring area is divided into erosion intensity zones; Based on the erosion intensity zoning results, spatial overlay analysis was conducted in combination with vegetation spatial pattern data to determine the erosion control effect coefficient of each vegetation coverage zone. Based on the erosion control effect coefficient and the slope crack feature set, an erosion risk assessment model is established to output the erosion sensitivity classification of the monitoring sample area and the identification results of key protection areas, and generate a dynamic monitoring report.

2. The method for dynamic monitoring of arsenic sandstone slope erosion according to claim 1, wherein: Generating a benchmark digital elevation model and identifying vegetation spatial patterns includes: A monitoring sample area was set up on the arsenic sandstone slope, and the coordinates of the sample area boundary were determined using positioning technology to establish a reference coordinate system; A ground-based three-dimensional laser scanner was used to scan the monitoring sample area to obtain initial terrain point cloud data; Preprocessing and rasterizing the initial terrain point cloud data to generate a reference digital elevation model, and calculating terrain factors based on the reference digital elevation model; Feature extraction is performed on multispectral remote sensing image data. Combined with the initial terrain point cloud data, a fusion classification algorithm is used to identify vegetation areas and bare soil areas to obtain vegetation spatial distribution data. Calculating vegetation coverage according to the vegetation spatial distribution data, and performing multi-level partitioning according to the vegetation coverage; Based on the vegetation spatial distribution data and the terrain factors, a landscape pattern analysis method is used to calculate the landscape pattern index of each vegetation coverage zone to form vegetation spatial pattern data.

3. The method for dynamic monitoring of arsenic sandstone slope erosion according to claim 1, wherein: The preset trigger condition includes the rainfall intensity reaching a preset trigger intensity threshold or the accumulated rainfall amount within a preset time window reaching a preset trigger amount threshold.

4. The method for dynamic monitoring of arsenic sandstone slope erosion according to claim 1, wherein: Generating the current digital elevation model and obtaining the slope crack feature set through point cloud roughness analysis include: After receiving the trigger signal from the erosion monitoring program, the scanning interval is determined according to the current rainfall intensity; Controlling the ground three-dimensional laser scanner according to the scanning interval to perform time-series scanning on the monitoring sample area to obtain real-time terrain point cloud data; Performing spatial registration and rasterization processing on the real-time terrain point cloud data to generate a current digital elevation model; extracting a surface roughness index based on the current digital elevation model and identifying slope crack areas through multi-scale roughness analysis; The slope crack area is vectorized, and spatial analysis is performed based on the vectorization results to determine the crack density, crack connectivity and crack direction, thereby generating a slope crack feature set.

5. The method for dynamic monitoring of arsenic sandstone slope erosion according to claim 1, wherein: The erosion intensity zoning of the monitoring sample area includes: Calculate the point-by-point difference between the current digital elevation model and the benchmark digital elevation model to obtain the distribution of elevation changes; Determining an erosion intensity classification threshold based on statistical characteristics of the elevation change distribution; The monitoring sample area is spatially partitioned according to the erosion intensity classification threshold, and the partition boundaries are optimized in combination with terrain factors to form erosion intensity partitions.

6. The method for dynamic monitoring of arsenic sandstone slope erosion according to claim 1, wherein: The spatial overlay analysis performed in combination with vegetation spatial pattern data includes: The layer overlay algorithm is used to spatially overlay the vegetation spatial pattern data and the erosion intensity zoning results to generate a vegetation-erosion composite layer; Based on the vegetation-erosion composite layer, statistically analyzing the area distribution of different erosion intensity levels within each vegetation coverage zone, and calculating the erosion intensity index of each vegetation coverage zone; Taking the erosion intensity index of the bare soil area as a benchmark reference, the erosion control effect coefficient of each vegetation coverage zone was calculated; Based on the landscape pattern index of each vegetation coverage zone, the erosion control effect coefficient is corrected using a weighted correction method to obtain a corrected erosion control effect coefficient; Establish a correspondence table between vegetation coverage zones and modified erosion control effect coefficients.

7. The method for dynamic monitoring of arsenic sandstone slope erosion according to claim 1, wherein: The establishment of the erosion risk assessment model comprises: Based on the erosion control effect coefficient and slope crack feature set, a multi-factor erosion risk assessment index system is constructed; The weighted superposition algorithm with adaptive weight optimization is used to calculate the multi-factor erosion risk assessment index system and generate a comprehensive erosion risk index distribution map; Based on the comprehensive erosion risk index distribution map, the fuzzy C-means clustering algorithm was used to divide the monitoring area into multiple erosion sensitivity levels to generate erosion sensitivity classification results. For the high sensitivity level and medium-high sensitivity level in the erosion sensitivity classification results, a fracture network topology structure is constructed based on the fracture feature set, and a graph theory algorithm is used to calculate the risk propagation path and identify key protection areas; Key protection areas are prioritized based on a multi-criteria approach, based on comprehensive erosion risk index and risk transmission potential, and a dynamic monitoring report is generated.

8. A dynamic monitoring system for arsenic sandstone slope erosion, based on the dynamic monitoring method for arsenic sandstone slope erosion according to any one of claims 1 to 7, characterized in that: include, The initial terrain acquisition module is used to establish a monitoring sample area on the arsenic sandstone slope, use a 3D laser scanner to obtain initial terrain point cloud data, generate a baseline digital elevation model, and identify the spatial pattern of vegetation; The meteorological monitoring module is used to set up a meteorological monitoring station to collect rainfall data in real time. When the rainfall parameters meet the preset trigger conditions, the erosion monitoring program is automatically triggered; The terrain change monitoring module is used to obtain real-time terrain point cloud data, generate the current digital elevation model, and obtain the slope crack feature set through point cloud roughness analysis; The erosion intensity zoning module is used to zonate the erosion intensity of the monitoring area based on the elevation change between the current digital elevation model and the benchmark digital elevation model; The erosion control analysis module is used to perform spatial overlay analysis based on the erosion intensity zoning results and combined with vegetation spatial pattern data to determine the erosion control effect coefficient of each vegetation coverage zone; The risk assessment module is used to establish an erosion risk assessment model based on the erosion control effect coefficient and the slope crack feature set, output the erosion sensitivity classification of the monitoring sample area and the identification results of key protection areas, and generate a dynamic monitoring report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for dynamically monitoring erosion of arsenic sandstone slopes according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for dynamic monitoring of arsenic sandstone slope erosion according to any one of claims 1 to 7 are implemented.

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