Methods and Systems for Dynamic Monitoring of Arsenic Sandstone Slope Erosion

Abstract

This invention discloses a method and system for dynamic monitoring of arsenic-bearing sandstone slope erosion, belonging to the field of geological disaster monitoring technology. The method includes establishing monitoring sample areas on arsenic-bearing sandstone slopes, acquiring initial topographic point cloud data, generating a baseline digital elevation model (DEM), and identifying vegetation spatial patterns; real-time collection of rainfall data, triggering an erosion monitoring program when rainfall parameters meet preset trigger conditions; acquiring real-time topographic point cloud data, generating a current DEM, and obtaining a slope fissure feature set; classifying erosion intensity based on the elevation change between the current DEM and the baseline DEM; determining the erosion control effect coefficient of each vegetation coverage zone by combining vegetation spatial pattern data; establishing an erosion risk assessment model, and outputting the erosion sensitivity classification and key protection area identification results for the monitoring sample areas. This invention achieves automated, high-precision dynamic monitoring of arsenic-bearing sandstone slope erosion, providing a scientific basis for soil and water conservation and geological disaster prevention.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring technology, and in particular to a method and system for dynamic monitoring of erosion on sandstone slopes. Background Technology

[0002] Arsenic sandstone, a typical soft rock, is widely distributed in the Loess Plateau region of Northwest China. It is characterized by weak erosion resistance and easy weathering and fragmentation. Under the influence of external forces such as rainfall, arsenic sandstone slopes are highly susceptible to soil erosion, causing serious impacts on the local ecological environment and engineering construction. Therefore, effective monitoring and early warning of erosion processes on arsenic sandstone slopes are of great significance.

[0003] Currently, slope erosion monitoring methods mainly include traditional ground surveying and near-surface laser scanning. Traditional ground surveying involves setting up monitoring facilities such as erosion needles and stakes on the slope and periodically conducting manual measurements to obtain topographic change data. Near-surface laser scanning technology uses laser scanners to acquire high-precision three-dimensional point cloud data of the slope, and analyzes topographic changes by comparing scan data from different periods. While these methods have been applied to slope erosion monitoring, they still have shortcomings in real-time response to rainfall events and quantifying the effects of vegetation regulation. Summary of the Invention

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

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

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for dynamic monitoring of arsenic-bearing sandstone slope erosion, comprising:

[0008] A monitoring sample area was established on the sandstone slope. Initial topographic point cloud data was obtained using a 3D laser scanner to generate a benchmark digital elevation model and identify the spatial pattern of vegetation.

[0009] The meteorological monitoring station is set up to collect rainfall data in real time. When the rainfall parameters meet the preset trigger conditions, the erosion monitoring program is automatically triggered.

[0010] 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;

[0011] Based on the elevation changes between the current digital elevation model and the benchmark digital elevation model, the monitoring sample area is divided into erosion intensity zones.

[0012] Based on the erosion intensity zoning results, spatial overlay analysis was performed using vegetation spatial pattern data to determine the erosion regulation effect coefficient of each vegetation coverage zone.

[0013] Based on the erosion control effect coefficient and the slope fissure feature set, an erosion risk assessment model is established, which outputs the erosion sensitivity classification of the monitoring sample area and the identification results of key protection areas, and generates a dynamic monitoring report.

[0014] As a preferred embodiment of the dynamic monitoring method for arsenic-rich sandstone slope erosion of the present invention, the method includes: generating a benchmark digital elevation model and identifying vegetation spatial patterns.

[0015] Monitoring sample areas were set up on the sandstone slope of arsenic trioxide, and the boundary coordinates of the sample areas were determined by positioning technology to establish a benchmark coordinate system;

[0016] The monitoring sample area was scanned using a terrestrial 3D laser scanner to obtain initial topographic point cloud data;

[0017] The initial terrain point cloud data is preprocessed and rasterized to generate a benchmark digital elevation model, and terrain factors are calculated based on the benchmark digital elevation model.

[0018] Feature extraction was performed on multispectral remote sensing image data, and combined with initial topographic point cloud data, a fusion classification algorithm was used to identify vegetated areas and bare soil areas to obtain vegetation spatial distribution data.

[0019] Vegetation coverage is calculated based on spatial distribution data, and multi-level zoning is performed based on vegetation coverage.

[0020] Based on vegetation spatial distribution data and topographic factors, landscape pattern analysis methods are used to calculate the landscape pattern index of each vegetation coverage zone, thus forming vegetation spatial pattern data.

[0021] As a preferred embodiment of the dynamic monitoring method for arsenic sandstone slope erosion of the present invention, the preset triggering conditions include rainfall intensity reaching a preset triggering intensity threshold or cumulative rainfall reaching a preset triggering amount threshold within a preset time window.

[0022] As a preferred embodiment of the dynamic monitoring method for arsenic-bearing sandstone slope erosion of the present invention, the method includes: generating a current digital elevation model and obtaining a slope crack feature set through point cloud roughness analysis, which includes:

[0023] After receiving the erosion monitoring program trigger signal, the scanning interval time is determined based on the current rainfall intensity;

[0024] The ground-based three-dimensional laser scanner is controlled according to the scanning interval to perform time-series scanning of the monitoring sample area and obtain real-time terrain point cloud data.

[0025] Spatial registration and rasterization are performed on real-time terrain point cloud data to generate the current digital elevation model;

[0026] Surface roughness index is extracted based on the current digital elevation model, and slope crack regions are identified through multi-scale roughness analysis;

[0027] The slope fissure region is vectorized, and spatial analysis is performed based on the vectorization results to determine the fissure density, fissure connectivity, and fissure orientation, thereby generating a slope fissure feature set.

[0028] As a preferred embodiment of the dynamic monitoring method for arsenic-rich sandstone slope erosion of the present invention, the method includes: dividing the monitoring sample area into erosion intensity zones, which includes:

[0029] The current digital elevation model and the benchmark digital elevation model are interpolated point by point to obtain the distribution of elevation changes.

[0030] The threshold for erosion intensity classification is determined based on the statistical characteristics of the distribution of elevation changes.

[0031] The monitoring sample area is spatially partitioned based on the erosion intensity classification threshold, and the partition boundaries are optimized by combining topographic factors to form erosion intensity partitions.

[0032] As a preferred embodiment of the dynamic monitoring method for arsenic-rich sandstone slope erosion of the present invention, the method includes: spatial overlay analysis combining vegetation spatial pattern data, which includes:

[0033] A layer overlay algorithm is used to spatially overlay vegetation spatial pattern data and erosion intensity zoning results to generate a vegetation-erosion composite layer.

[0034] Based on the vegetation-erosion composite layer, the area distribution of different erosion intensity levels within each vegetation coverage zone is statistically analyzed, and the erosion intensity index of each vegetation coverage zone is calculated.

[0035] Using the erosion intensity index of the bare soil area as a benchmark, the erosion control effect coefficient of each vegetation coverage zone was calculated.

[0036] 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 the corrected erosion control effect coefficient.

[0037] Establish a table showing the correspondence between vegetation cover zones and the modified erosion control effect coefficient.

[0038] As a preferred embodiment of the dynamic monitoring method for arsenic sandstone slope erosion of the present invention, a multi-factor erosion risk assessment index system is constructed based on the erosion regulation effect coefficient and the slope fissure feature set.

[0039] An adaptive weighted superposition algorithm was used to calculate the multi-factor erosion risk assessment index system and generate a comprehensive erosion risk index distribution map.

[0040] Based on the comprehensive erosion risk index distribution map, the monitoring sample area is divided into multiple erosion sensitivity levels using the fuzzy C-means clustering algorithm, generating erosion sensitivity classification results.

[0041] For the high-sensitivity and medium-high-sensitivity levels in the erosion sensitivity classification results, a fracture network topology 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.

[0042] Key protection areas are prioritized based on a multi-criteria system, including a comprehensive erosion risk index and risk transmission potential, and dynamic monitoring reports are generated.

[0043] Secondly, the present invention provides a dynamic monitoring system for arsenic-bearing sandstone slope erosion, comprising:

[0044] The initial terrain acquisition module is used to establish monitoring sample areas on sandstone slopes. It uses a 3D laser scanner to acquire initial terrain point cloud data, generate a benchmark digital elevation model, and identify vegetation spatial patterns.

[0045] The meteorological monitoring module is used to set up the 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.

[0046] The terrain change monitoring module is used to 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.

[0047] The erosion intensity zoning module is used to zon the monitoring sample area based on the elevation change between the current digital elevation model and the benchmark digital elevation model.

[0048] The erosion control analysis module is used to perform spatial overlay analysis based on the erosion intensity zoning results and vegetation spatial pattern data to determine the erosion control effect coefficient of each vegetation coverage zone.

[0049] 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.

[0050] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the dynamic monitoring method for arsenic sandstone slope erosion as described in the first aspect of the present invention.

[0051] Fourthly, 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, it implements any step of the dynamic monitoring method for arsenic sandstone slope erosion as described in the first aspect of the present invention.

[0052] The beneficial effects of this invention are as follows: By establishing an automatic monitoring mechanism triggered by rainfall, combined with real-time meteorological data acquisition and dynamic scanning frequency adjustment, precise capture of critical erosion periods is achieved, improving the targeting and timeliness of monitoring work. By integrating three-dimensional laser scanning, multispectral remote sensing imagery, and point cloud roughness analysis technology, coordinated monitoring of topographic changes, vegetation patterns, and fracture characteristics is realized, improving monitoring accuracy and data reliability in the complex environment of sandstone slopes. By constructing a quantitative model of vegetation erosion regulation effects and a multi-factor risk assessment system, a complete technical chain from data acquisition to risk early warning is realized, improving the scientific nature of erosion sensitivity identification and the accuracy of key protection area delineation, providing systematic technical support for soil and water conservation decision-making in sandstone areas. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of 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.

[0054] Figure 1 This is a flowchart of a method for dynamic monitoring of arsenic sandstone slope erosion.

[0055] Figure 2 A flowchart illustrating the process of obtaining the slope fissure feature set for a dynamic monitoring method of arsenic sandstone slope erosion.

[0056] Figure 3 A flowchart for constructing an erosion risk assessment model for dynamic monitoring of arsenic sandstone slope erosion.

[0057] Figure 4 This is a schematic diagram of the modules of a dynamic monitoring system for arsenic sandstone slope erosion. Detailed Implementation

[0058] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0059] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the term "one embodiment" or "embodiment" as used 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 different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0061] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for dynamic monitoring of arsenic sandstone slope erosion, the flowchart of which is shown below. Figure 1 As shown, it includes the following steps:

[0062] S1: Establish a monitoring sample area on the sandstone slope, use a 3D laser scanner to acquire initial topographic point cloud data, generate a benchmark digital elevation model and identify the spatial pattern of vegetation.

[0063] Specifically, step S1 includes:

[0064] S1.1: Set up monitoring sample areas on the arsenic sandstone slope, use positioning technology to determine the boundary coordinates of the sample areas, and establish a benchmark coordinate system.

[0065] In one embodiment, a monitoring sample area is established on a slope with typical arsenic sandstone geological characteristics. The sample area should include sections with different vegetation cover to analyze the regulatory effect of vegetation on erosion. The area of ​​the monitoring sample area is usually 100-500 square meters. RTK-GPS equipment is used to measure the boundary coordinates and set control points to establish a unified reference coordinate system.

[0066] S1.2: Use a ground-based three-dimensional laser scanner to scan the monitoring sample area and obtain initial topographic point cloud data.

[0067] The initial terrain point cloud data includes three-dimensional coordinate information and reflection intensity information.

[0068] 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.

[0069] The preprocessing includes outlier removal and noise filtering, and the terrain factors include slope, aspect, and surface roughness. Specifically, the slope is calculated using the maximum slope algorithm, the aspect is calculated using the gradient direction method, and the surface roughness is obtained by calculating the standard deviation of elevation within the grid window.

[0070] S1.4: Feature extraction is performed on multispectral remote sensing image data. Combined with initial topographic point cloud data, a fusion classification algorithm is used to identify vegetated areas and bare soil areas to obtain vegetation spatial distribution data.

[0071] In one embodiment, multispectral remote sensing image data of the monitoring sample area can be acquired using UAV multispectral imaging or high-resolution satellite remote sensing data. Vegetation spatial distribution data can be updated periodically according to monitoring needs. The update cycle is determined based on vegetation growth characteristics, monitoring accuracy requirements, and the frequency of rainfall events, generally monthly, with event-triggered updates following heavy rainfall events.

[0072] Specifically, radiometric and geometric correction preprocessing is performed on multispectral remote sensing image data to eliminate atmospheric scattering and geometric distortion, resulting in standardized multispectral image data. Based on this standardized multispectral image data, the Normalized Difference Vegetation Index (NDV), Enhanced Vegetation Index (EDI), and Soil-Regulated Vegetation Index (SRI) are calculated to form a multidimensional vegetation index feature vector. The NDC is obtained by standardizing the difference between the near-infrared and red bands; the EDI is obtained through a multi-band optimization algorithm incorporating an atmospheric correction factor; and the SRI is obtained by eliminating background interference through a soil brightness correction factor. Considering the characteristics of exposed soil and low vegetation cover in the arsenic-rich sandstone region, the combination of the NDC, EDI, and SRI can cover the full range of identification needs from bare soil to high-coverage vegetation.

[0073] Furthermore, the initial topographic point cloud data is projected onto the multispectral image coordinate system, and spatial registration and fusion of the point cloud data and multispectral image data are achieved through nearest neighbor interpolation. Reflectance intensity information is extracted from the point cloud data, and a spectral-intensity fusion feature set is constructed by combining it with multidimensional vegetation index feature vectors. Based on the spectral-intensity fusion feature set, a support vector machine classification algorithm is used to perform pixel-level classification of the monitored sample areas, identifying vegetated areas and bare soil areas. Morphological filtering and connectivity analysis are performed on the classification results to remove isolated noise pixels and smooth area boundaries, generating vegetation spatial distribution data.

[0074] S1.5: Calculate vegetation coverage based on vegetation spatial distribution data, and perform multi-level zoning based on vegetation coverage.

[0075] It should be noted that the multi-level zoning includes bare soil areas, low-coverage areas, medium-coverage areas, and high-coverage areas. Considering the special characteristics of the sandstone area, the zoning is based on vegetation coverage thresholds: bare soil areas have less than 3% vegetation coverage, low-coverage areas have 3%-20% vegetation coverage, medium-coverage areas have 20%-40% vegetation coverage, and high-coverage areas have more than 40% vegetation coverage.

[0076] S1.6: Based on vegetation spatial distribution data and topographic factors, landscape pattern analysis methods are used to calculate the landscape pattern index of each vegetation coverage zone, forming vegetation spatial pattern data.

[0077] Preferably, the landscape pattern index includes patch density, connectivity index, and landscape shape index.

[0078] Specifically, the calculation process of the landscape pattern index is as follows: First, based on the vegetation cover zoning results, patch identification is performed. An eight-neighbor connectivity algorithm is used to aggregate adjacent pixels of the same cover type into independent patches, and the number and area distribution of patches within each zone are statistically analyzed. Second, the patch density index is calculated, obtained by the ratio of the total number of patches within each vegetation cover zone to the total area of ​​the zone. Third, the connectivity index is calculated, using the fixed buffer distance method to measure the spatial distance between similar vegetation patches, and assessing the spatial connectivity between vegetation patches by the buffer overlap area ratio. Finally, the landscape shape index is calculated, based on the perimeter-to-area ratio of each patch, calculating the roundness index and performing standardization processing to assess the shape complexity and edge effect of vegetation patches. Combining slope and aspect information from topographic factors, topographic weighting corrections are applied to each landscape pattern index to generate comprehensive vegetation spatial pattern data.

[0079] It should be noted that patch density, connectivity index, and landscape shape index were chosen because these three indicators can effectively reflect the regulatory role of vegetation spatial distribution on the erosion process. Specifically, patch density reflects the continuity of vegetation distribution; better continuity in vegetation cover provides a more stable overall protective effect. The connectivity index reflects the spatial connectivity between vegetation patches; better connectivity results in a stronger overall soil and water conservation effect. The landscape shape index reflects the regularity of vegetation patches; more regular vegetation patches have better cover stability and can effectively reduce the adverse effects of edge effects on erosion resistance.

[0080] 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 will be automatically triggered.

[0081] The preset triggering conditions include rainfall intensity reaching a preset triggering intensity threshold or cumulative rainfall reaching a preset triggering amount threshold within a preset time window (e.g., 24 hours). The preset triggering intensity threshold is a critical rainfall intensity value determined based on local historical rainfall data and statistical analysis of erosion events. The preset triggering amount threshold is a critical cumulative rainfall value within a preset time window determined based on regional climate characteristics and topographic conditions.

[0082] It should be noted that the use of both rainfall intensity and cumulative rainfall as dual triggering conditions is based on the physical mechanism of sandstone slope erosion. Rainfall intensity determines the initiation conditions for soil stripping, while cumulative rainfall determines the formation conditions for surface runoff. These two parameters correspond to different stages of the erosion process. Compared to single-parameter triggering, this dual-trigger mechanism can more accurately identify genuine erosion events, reduce false triggers caused by the complexity of rainfall characteristics, improve the accuracy and reliability of monitoring, and realize the transformation from periodic monitoring to precise event-driven monitoring.

[0083] S3: After the erosion monitoring program is triggered, a ground-based 3D laser scanner is used to scan the monitoring sample area to obtain real-time topographic point cloud data, generate the current digital elevation model, and obtain the slope crack feature set through point cloud roughness analysis.

[0084] Specifically, the flowchart for obtaining the slope crack feature set is as follows: Figure 2 As shown, it includes:

[0085] S3.1: After receiving the erosion monitoring program trigger signal, determine the scanning interval time based on the current rainfall intensity.

[0086] 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 ensures the data acquisition density during critical periods while avoiding unnecessary equipment wear and data redundancy.

[0087] S3.2: Control the ground-based three-dimensional laser scanner according to the scanning interval to perform time-series scanning of the monitoring sample area and obtain real-time terrain point cloud data.

[0088] S3.3: Perform spatial registration and rasterization on real-time terrain point cloud data to generate the current digital elevation model.

[0089] Specifically, coarse preprocessing is performed on the real-time terrain point cloud data, identifying and removing outliers with obvious anomalies based on distance thresholds and density statistics. Stable feature points are extracted as control points based on a benchmark digital elevation model, and a feature descriptor matching algorithm is used to identify the corresponding control points in the preprocessed real-time terrain point cloud data. The iterative nearest point algorithm is used to perform precise registration of the control points, calculate the three-dimensional rigid body transformation matrix, and unify the real-time terrain point cloud data to the benchmark coordinate system.

[0090] Furthermore, the registered point cloud data undergoes refined environmental interference correction. Density clustering algorithm is used to identify and remove abnormal scattering points and noise interference. Statistical filtering algorithm is employed for noise filtering, and adaptive compensation correction is applied to the point cloud reflection intensity based on rainfall intensity and laser wavelength characteristics. Natural neighborhood interpolation algorithm is used to rasterize the corrected point cloud data, generating a current digital elevation model with the same resolution and spatial range as the baseline digital elevation model.

[0091] By employing the aforementioned phased processing strategy, data interference under rainfall conditions is effectively eliminated while ensuring registration accuracy, thereby improving the generation quality and reliability of the current digital elevation model.

[0092] S3.4: Extract the surface roughness index based on the current digital elevation model, and identify the slope crack region through multi-scale roughness analysis.

[0093] Specifically, the topographic relief of the grid cells 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, and the surface roughness index is calculated at each window scale, forming a multi-scale roughness dataset. Differential analysis is performed on the multi-scale roughness dataset to identify roughness anomaly regions as potential fracture candidate areas. Combining topographic concavity / convexity analysis and slope aspect information, and based on an adaptive roughness threshold and spatial connectivity constraints, potential fracture candidate areas are screened and merged to extract slope fracture regions.

[0094] Optionally, morphological filtering and geometric feature constraints are used to optimize the extracted slope crack regions to determine the final distribution of slope crack regions.

[0095] It is worth noting that traditional fracture identification methods mainly rely on manual visual interpretation or image processing techniques, which are difficult to accurately identify the micro-fracture structure of sandstone slopes. This invention, through multi-scale roughness analysis, can automatically identify fracture features at different scales. Combined with topographic concavity / convexity analysis and spatial connectivity constraints, it improves the accuracy and reliability of fracture identification on sandstone slopes. This technology provides key topographic structural parameters for subsequent erosion risk assessment and is the technical foundation for erosion monitoring.

[0096] S3.5: Vectorize the slope crack region, and perform spatial analysis based on the vectorization results to determine crack density, crack connectivity and crack orientation, and generate a slope crack feature set.

[0097] Furthermore, a skeleton extraction algorithm is used to vectorize the slope crack region, extracting the centerline structure of the crack region. Based on the vectorized crack geometry, spatial density analysis is used to calculate the crack area ratio and number of cracks per unit area, determining the crack density. Spatial topology analysis is used to assess the spatial connectivity between crack centerlines, calculating the crack connectivity index. A statistical method for crack centerline direction is used to extract the main extension directions of the crack region, determining the crack orientation distribution. Based on the above spatial analysis results, a slope crack feature set is established.

[0098] S4: Based on the elevation changes between the current digital elevation model and the benchmark digital elevation model, the monitoring sample area is divided into erosion intensity zones.

[0099] Specifically, step S4 includes:

[0100] S4.1: Perform point-by-point difference calculations between the current digital elevation model and the benchmark digital elevation model to obtain the distribution of elevation changes.

[0101] S4.2: Determine the threshold for erosion intensity classification based on the statistical characteristics of the distribution of elevation change.

[0102] In one embodiment, the process of determining the erosion intensity grading threshold includes: calculating the statistical parameters of the elevation change distribution, including the mean, standard deviation, and skewness; setting the initial grading threshold using the standard deviation grading method, with the mean as the center, and then... , , A multi-level classification system is established using standard deviation multiples of the intervals. 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 extreme value intervals. The 5th and 95th quantile values ​​are calculated to optimize the boundaries of the initial classification thresholds. The classification thresholds are then validated and adjusted, and further modified according to specific application scenarios, with the adjustment not exceeding 15% of the original threshold, thus establishing the final erosion intensity classification threshold system.

[0103] S4.3: Spatial partitioning of the monitoring sample area is performed based on the erosion intensity classification threshold, and the partition boundaries are optimized by combining topographic factors to form erosion intensity partitions.

[0104] Optionally, the erosion intensity zoning includes sedimentary zones, slightly eroded zones, lightly eroded zones, moderately eroded zones, and heavily eroded zones. In other embodiments, other grading methods may also be adopted as needed, such as three-level zoning (lightly, moderately, and heavily eroded) or seven-level zoning.

[0105] Preferably, the zoning boundary optimization process incorporating topographic factors includes: comprehensively considering topographic factors such as slope, aspect, and surface roughness, and identifying areas with significant changes in topographic features as natural boundaries. For transitional areas where topographic factors gradually change, boundary smoothing is performed; adjacent areas with similar topographic factors within the same erosion intensity level are merged, eliminating fragmented zoning caused by local fluctuations, and forming erosion intensity zoning with clear boundaries and spatial continuity.

[0106] Ideally, by calculating elevation changes, determining intelligent thresholds, and optimizing topographic factors, the accuracy and reliability of erosion intensity zoning can be improved. The intelligent threshold determination mechanism reduces errors from subjective human judgment, topographic factor boundary optimization eliminates unreasonable boundaries generated by statistical grading, and automated processing improves work efficiency.

[0107] S5: Based on the erosion intensity zoning results, spatial overlay analysis is performed in conjunction with vegetation spatial pattern data to determine the erosion control effect coefficient of each vegetation coverage zone.

[0108] It should be noted that the spatial overlay analysis based on vegetation spatial pattern data and erosion intensity zoning results is a significant innovation of this invention. Traditional erosion assessment methods often consider vegetation as a single factor, neglecting the impact of vegetation spatial configuration on erosion regulation. This invention achieves precise matching between vegetation spatial pattern and erosion intensity zoning through a layer overlay algorithm, quantitatively determines the erosion regulation effect coefficient of each vegetation cover zone, and corrects it by combining it with the landscape pattern index, revealing the regulation mechanism of vegetation spatial configuration on slope erosion. This method provides key vegetation regulation parameters for erosion risk assessment models, realizing the transformation from a single vegetation factor to a vegetation-erosion coupled analysis.

[0109] Specifically, the S5 steps include:

[0110] S5.1: A layer overlay algorithm is used to spatially overlay vegetation spatial pattern data and erosion intensity zoning results to generate a vegetation-erosion composite layer.

[0111] Preferably, before layer overlay, the vegetation spatial pattern data and erosion intensity zoning results are first subjected to geometric correction and projection unification processing. A raster data joint analysis method is used to generate composite attribute values ​​through pixel-level attribute combinations, and the fragmented patches in the overlay results are optimized.

[0112] S5.2: Based on the vegetation-erosion composite layer, statistically analyze the area distribution of different erosion intensity levels within each vegetation coverage zone, and calculate the erosion intensity index of each vegetation coverage zone.

[0113] 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. The weight coefficient is set to increase according to the erosion intensity level.

[0114] S5.3: Using the erosion intensity index of the bare soil area as a benchmark, calculate the erosion control effect coefficient of each vegetation coverage zone.

[0115] The erosion control effect coefficient was determined using a benchmark comparison method, which quantifies the control effect intensity of each vegetation cover zone relative to the bare soil zone benchmark. When there is no obvious bare soil zone in the study area, the zone with the lowest vegetation cover can be selected as the benchmark reference.

[0116] 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 the corrected erosion control effect coefficient.

[0117] Specifically, the entropy weight method was used to determine the weight coefficients of patch density, connectivity index, and landscape shape index. After normalization, the weighted summation yielded 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 corrected erosion control effect coefficient. By correcting the landscape pattern characteristics, the spatial accuracy and ecological rationality of the erosion control effect coefficient were improved.

[0118] S5.5: Establish a table showing the correspondence between vegetation cover zones and the modified erosion control effect coefficient.

[0119] Ideally, by employing vegetation-erosion spatial overlay analysis, quantifying erosion control effects, and correcting landscape patterns, the regulatory effect of vegetation on soil erosion can be accurately assessed. Layer overlay algorithms achieve precise matching between vegetation spatial patterns and erosion intensity zoning, improving the statistical accuracy of erosion intensity distribution characteristics in each vegetation cover zone. Using bare soil areas as a benchmark, the erosion control effect coefficient is calculated and weighted by combining it with landscape pattern indices, accurately quantifying the net erosion protection effect of vegetation. This approach considers the impact of vegetation spatial configuration patterns on erosion control effects, enhancing the ecological rationality and spatial representativeness of the assessment results.

[0120] S6: Based on the erosion control effect coefficient and the slope crack feature set, establish an erosion risk assessment model, output the erosion sensitivity classification of the monitoring sample area and the identification results of key protection areas, and generate a dynamic monitoring report.

[0121] The core innovation of this invention lies in establishing an erosion risk assessment model based on the erosion regulation effect coefficient and the slope fissure feature set. Traditional erosion risk assessments are mostly based on single factors or simple empirical formulas, which are difficult to fully reflect the complexity of sandstone slope erosion. This invention organically combines vegetation regulation effects and topographic structural features. The erosion regulation effect coefficient integrates the coupling relationship between erosion intensity zoning and vegetation spatial pattern, reflecting the differentiated regulation ability of vegetation on areas with different erosion levels; the slope fissure feature set reflects the control effect of geological structure on erosion sensitivity.

[0122] Building upon this foundation, adaptive weight optimization and fuzzy clustering techniques were employed to quantitatively express the interaction mechanism between vegetation regulation effects and topographic structural features, thus constructing a multi-factor integrated erosion risk assessment model. This model can accurately classify erosion sensitivity and intelligently identify key protection areas, completing the transformation from single-factor static assessment to multi-factor dynamic comprehensive assessment, and realizing the shift from post-event analysis to pre-event early warning, providing scientific decision support for erosion protection of sandstone slopes.

[0123] Specifically, the flowchart for constructing the erosion risk assessment model is as follows: Figure 3 As shown, it includes:

[0124] S6.1: Based on the erosion control effect coefficient and the slope crack feature set, a multi-factor erosion risk assessment index system is constructed.

[0125] Optionally, the erosion control effect coefficient is converted into a vegetation control risk index, and the fracture density, fracture connectivity and fracture orientation are converted into geological structure risk indicators. Each risk index is standardized and spatially registered, and then classified and organized according to vegetation control category and geological structure category.

[0126] S6.2: The multi-factor erosion risk assessment index system is calculated using an adaptive weight optimization weighted superposition algorithm to generate a comprehensive erosion risk index distribution map.

[0127] The adaptive weight optimization dynamically adjusts the weight coefficients of each evaluation indicator based on the current rainfall intensity and topographic factors. Specifically, a weight adjustment function is constructed based on the current rainfall intensity and topographic factors to dynamically adjust the weight coefficients of vegetation regulation indicators 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.

[0128] Ideally, the algorithm can dynamically adjust the weights of evaluation indicators based on real-time environmental conditions, making the erosion risk assessment results more consistent with actual environmental changes and enhancing the model's adaptability to different seasons and climatic conditions.

[0129] S6.3: Based on the comprehensive erosion risk index distribution map, the monitoring sample area is divided into multiple erosion sensitivity levels using the fuzzy C-means clustering algorithm, generating erosion sensitivity classification results.

[0130] Furthermore, risk index values ​​of pixels are extracted from the comprehensive erosion risk index distribution map to construct a sample dataset, and the number of clusters is set to correspond to the number of erosion sensitivity levels. Fuzzy C-means clustering algorithm is used for iterative calculation, and each pixel is assigned to the corresponding cluster category according to the maximum membership principle. The clustering results are mapped to erosion sensitivity levels according to the risk index size of the cluster centers, generating an erosion sensitivity grading result map and a grading threshold table.

[0131] Optionally, the erosion sensitivity levels include high sensitivity, medium-high sensitivity, medium sensitivity, low sensitivity, and extremely low sensitivity. This algorithm effectively handles the ambiguous boundary problems between erosion sensitivity levels, providing smooth transition region divisions and avoiding abrupt boundary problems caused by hard grading.

[0132] S6.4: For the high-sensitivity and medium-high-sensitivity levels in the erosion sensitivity classification results, a fracture network topology 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.

[0133] Preferably, regions with a fracture density exceeding a preset density threshold are designated as network nodes. Network edges are established between adjacent regions where fracture connectivity exceeds a preset connectivity threshold. Weight coefficients of these network edges are calculated based on fracture orientation to generate a weighted fracture network topology. High-sensitivity and medium-high-sensitivity regions are designated as risk source nodes. A path algorithm is used to calculate the propagation path from each risk source node to other nodes. Risk propagation intensity is calculated based on network edge weights and path lengths to determine the dominant direction and scope of risk propagation. The cumulative risk propagation intensity value for each node is calculated, and nodes with a cumulative propagation intensity value exceeding a preset propagation threshold and their neighboring regions are identified as key protection areas.

[0134] Among them, the preset density threshold is determined by statistical analysis of the fracture density distribution characteristics, the preset connectivity threshold is determined by analysis of the fracture connectivity distribution law, and the preset propagation threshold is determined by statistical analysis of the risk propagation intensity distribution.

[0135] Preferably, by constructing a fracture network topology and employing graph theory algorithms, the propagation paths and key control nodes of erosion risks can be systematically identified, effectively revealing the spatial propagation mechanism of erosion risks within the region. This method overcomes the shortcomings of static sensitivity classification in reflecting the dynamic propagation of risks. By identifying key control nodes of risk propagation, it achieves a shift from identifying high-risk areas to selecting optimal protection locations, improving the scientific rigor and effectiveness of protective measures deployment.

[0136] S6.5: Prioritize key protection areas according to multiple criteria, including comprehensive erosion risk index and risk transmission potential.

[0137] It should be noted that multi-criteria prioritization refers to prioritizing key protection areas based on two evaluation criteria: comprehensive erosion risk index and risk transmission potential, using multi-criteria decision-making methods such as the analytic hierarchy process (AHP) or TOPSIS.

[0138] S6.6: Integrate erosion sensitivity classification results, key protection areas, and priority ranking to generate dynamic monitoring reports.

[0139] Ideally, multi-factor risk assessment, adaptive weight optimization, and sensitivity grading can achieve accurate identification and dynamic monitoring of erosion risks. 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 priority ranking system and generating dynamic monitoring reports enhances the systematic nature of erosion risk management and the scientific rigor of decision support.

[0140] This embodiment also provides a dynamic monitoring system for arsenic sandstone slope erosion, as shown in the module diagram below. Figure 4 As shown, it includes:

[0141] The initial terrain acquisition module is used to establish monitoring sample areas on sandstone slopes. It uses a 3D laser scanner to acquire initial terrain point cloud data, generate a benchmark digital elevation model, and identify vegetation spatial patterns.

[0142] The meteorological monitoring module is used to set up the 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.

[0143] The terrain change monitoring module is used to 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.

[0144] The erosion intensity zoning module is used to zon the monitoring sample area based on the elevation change between the current digital elevation model and the benchmark digital elevation model.

[0145] The erosion control analysis module is used to perform spatial overlay analysis based on the erosion intensity zoning results and vegetation spatial pattern data to determine the erosion control effect coefficient of each vegetation coverage zone.

[0146] 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.

[0147] This embodiment also provides a computer device applicable to 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 the computer-executable instructions to realize the dynamic monitoring method of arsenic sandstone slope erosion as proposed in the above embodiment.

[0148] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices 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 the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0149] This embodiment also provides a storage medium storing a computer program, 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 Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0150] In summary, this invention establishes an automatic monitoring mechanism triggered by rainfall, combined with real-time meteorological data acquisition and dynamic scanning frequency adjustment, to accurately capture critical periods of erosion, thus improving the targeting and timeliness of monitoring. By integrating 3D laser scanning, multispectral remote sensing imagery, and point cloud roughness analysis technologies, it achieves coordinated monitoring of topographic changes, vegetation patterns, and fissure characteristics, improving monitoring accuracy and data reliability in the complex environment of sandstone slopes. Furthermore, by constructing a quantitative model of vegetation erosion regulation effects and a multi-factor risk assessment system, it realizes a complete technical chain from data acquisition to risk warning, improving the scientific nature of erosion sensitivity identification and the accuracy of key protection area delineation, providing systematic technical support for soil and water conservation decision-making in sandstone areas.

[0151] 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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for dynamic monitoring of erosion on sandstone slopes, characterized by: include: A monitoring sample area was established on the sandstone slope. Initial topographic point cloud data was obtained using a 3D laser scanner to generate a benchmark digital elevation model and identify the spatial pattern of vegetation. The meteorological monitoring station is set up 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 changes between the current digital elevation model and the benchmark digital elevation model, the monitoring sample area is divided into erosion intensity zones. Based on the erosion intensity zoning results, spatial overlay analysis was performed using vegetation spatial pattern data to determine the erosion regulation effect coefficient of each vegetation coverage zone. Based on the erosion control effect coefficient and the slope fissure feature set, an erosion risk assessment model is established, which outputs the erosion sensitivity classification of the monitoring sample area and the identification results of key protection areas, and generates a dynamic monitoring report.

2. The method for dynamic monitoring of arsenic-rich sandstone slope erosion as described in claim 1, characterized in that: The process of generating a benchmark digital elevation model and identifying vegetation spatial patterns includes: Monitoring sample areas were set up on the sandstone slope of arsenic trioxide, and the boundary coordinates of the sample areas were determined by positioning technology to establish a benchmark coordinate system; The monitoring sample area was scanned using a terrestrial 3D laser scanner to obtain initial topographic point cloud data; The initial terrain point cloud data is preprocessed and rasterized to generate a reference digital elevation model, and terrain factors are calculated based on the reference digital elevation model. Feature extraction was performed on multispectral remote sensing image data, and combined with initial topographic point cloud data, a fusion classification algorithm was used to identify vegetated areas and bare soil areas to obtain vegetation spatial distribution data. The vegetation coverage is calculated based on the spatial distribution data of the vegetation, and multi-level zoning is performed based on the vegetation coverage. Based on the vegetation spatial distribution data and the topographic factors, the landscape pattern index of each vegetation coverage zone is calculated using the landscape pattern analysis method to form vegetation spatial pattern data.

3. The method for dynamic monitoring of arsenic sandstone slope erosion as described in claim 1, characterized in that: The preset triggering conditions include rainfall intensity reaching a preset triggering intensity threshold or cumulative rainfall reaching a preset triggering amount threshold within a preset time window.

4. The method for dynamic monitoring of arsenic-rich sandstone slope erosion as described in claim 1, characterized in that: The process of generating the current digital elevation model and obtaining the slope crack feature set through point cloud roughness analysis includes: After receiving the erosion monitoring program trigger signal, the scanning interval time is determined based on the current rainfall intensity; The ground-based three-dimensional laser scanner is controlled according to the scanning interval to perform time-series scanning of the monitoring sample area and obtain real-time terrain point cloud data. The real-time terrain point cloud data is spatially registered and rasterized to generate the current digital elevation model; The surface roughness index is extracted based on the current digital elevation model, and the slope crack region is identified through multi-scale roughness analysis. The slope fissure region is vectorized, and spatial analysis is performed based on the vectorization results to determine the fissure density, fissure connectivity, and fissure orientation, thereby generating a slope fissure feature set.

5. The method for dynamic monitoring of arsenic sandstone slope erosion as described in claim 1, characterized in that: The erosion intensity zoning of the monitored sample area includes: The current digital elevation model and the benchmark digital elevation model are interpolated point by point to obtain the distribution of elevation changes. The erosion intensity classification threshold is determined based on the statistical characteristics of the elevation change distribution. The monitoring sample area is spatially partitioned according to the erosion intensity grading threshold, and the partition boundaries are optimized by combining topographic factors to form erosion intensity partitions.

6. The method for dynamic monitoring of arsenic-rich sandstone slope erosion as described in claim 1, characterized in that: The spatial overlay analysis combining vegetation spatial pattern data includes: A layer overlay algorithm is used to spatially overlay vegetation spatial pattern data and 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 within each vegetation coverage zone is statistically analyzed, and the erosion intensity index of each vegetation coverage zone is calculated. Using the erosion intensity index of the bare soil area as a benchmark, 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 the corrected erosion control effect coefficient. Establish a table showing the correspondence between vegetation cover zones and the modified erosion control effect coefficient.

7. The method for dynamic monitoring of arsenic-rich sandstone slope erosion as described in claim 1, characterized in that: The establishment of the erosion risk assessment model includes: Based on the erosion regulation effect coefficient and the slope crack feature set, a multi-factor erosion risk assessment index system is constructed. An adaptive weighted superposition algorithm was 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 monitoring sample area is divided into multiple erosion sensitivity levels using the fuzzy C-means clustering algorithm, generating erosion sensitivity classification results. For the high-sensitivity and medium-high-sensitivity levels in the erosion sensitivity classification results, a fracture network topology 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 system, including a comprehensive erosion risk index and risk transmission potential, and dynamic monitoring reports are generated.

8. A dynamic monitoring system for arsenic-rich sandstone slope erosion, based on the dynamic monitoring method for arsenic-rich 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 monitoring sample areas on sandstone slopes. It uses a 3D laser scanner to acquire initial terrain point cloud data, generate a benchmark digital elevation model, and identify vegetation spatial patterns. The meteorological monitoring module is used to set up the 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 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. The erosion intensity zoning module is used to zon the monitoring sample 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 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, characterized in that: When the processor executes the computer program, it implements the steps of the dynamic monitoring method for arsenic sandstone slope erosion according to any one of claims 1 to 7.

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

Images