Method for monitoring stability of ecological protection slope of expressway

Abstract

The invention relates to the technical field of geological environment monitoring, in particular to a stability monitoring method for ecological slope protection of an expressway. The method comprises the following steps: acquiring remote sensing data of an expressway monitoring area; performing water and soil loss area detection according to the remote sensing data of the highway monitoring area to obtain water and soil loss area data; performing erosion intensity analysis according to the water and soil loss area data to obtain erosion intensity data; extracting vegetation coverage data of the water and soil loss area data; performing drainage clogging analysis according to the vegetation coverage data and the erosion intensity data to obtain drainage clogging data; performing soil stability analysis based on the drainage clogging data to obtain soil stability data; and detecting the soil compaction degree according to the soil stability data. Based on the geological environment monitoring technology, the detection and prediction precision of water and soil loss, soil stability and ecological slope protection stability of the highway monitoring area is improved, and the landslide prevention and ecological protection efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological environment monitoring, and in particular to a stability monitoring method for ecological slope protection of a highway. Background Art

[0002] Traditional stability monitoring methods for ecological slope protection on highways primarily rely on manual inspections and local physical sensor data to assess slope status. These methods suffer from limited data acquisition, poor timeliness, and an inability to fully reflect the overall stability of the ecological slope protection. Manual inspections are significantly affected by factors such as topography, climate, and personnel experience, making it difficult to continuously perceive large-scale, high-frequency, and multi-factor coupled changes. Locally deployed physical monitoring methods such as strain gauges and inclinometers, while providing real-time performance, are sparsely deployed and subject to installation constraints, making them difficult to cover the entire slope protection system and unable to effectively identify early-stage stability risks caused by soil erosion, vegetation degradation, and drainage blockage. Furthermore, existing methods often overlook the multi-source information fusion between remote sensing imagery and surface ecological characteristics, lacking the ability to analyze slope protection stability from multiple perspectives, including terrain evolution, vegetation changes, soil structure, and hydrological processes. This results in insufficient early warning accuracy and fails to meet the practical needs of ecological safety assurance for mountainous highways. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a stability monitoring method for ecological slope protection of highways to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a method for monitoring the stability of an ecological slope protection of a highway comprises the following steps:

[0005] Step S1: Acquire remote sensing data of the highway monitoring area; perform soil erosion area detection based on the remote sensing data of the highway monitoring area to obtain soil erosion area data;

[0006] Step S2: performing erosion intensity analysis based on the soil erosion area data to obtain erosion intensity data; extracting vegetation coverage data from the soil erosion area data; performing drainage blockage analysis based on the vegetation coverage data and erosion intensity data to obtain drainage blockage data;

[0007] Step S3: performing soil stability analysis based on the drainage blockage data to obtain soil stability data; detecting soil compaction based on the soil stability data; performing soil landslide simulation based on the soil compaction to obtain soil landslide data;

[0008] Step S4: Identify ecological slope protection areas based on the soil and water loss area data to obtain ecological slope protection data; perform ecological slope protection stability analysis based on the ecological slope protection data and soil landslide data to obtain ecological slope protection stability data.

[0009] The present invention efficiently acquires a wide range of surface data based on remote sensing images, thereby improving the perception of the macro-state of the slope protection area and effectively identifying key areas prone to soil erosion. By analyzing the erosion intensity and vegetation coverage in the soil erosion area, drainage anomalies and vegetation degradation locations are identified, thereby capturing early signs of blocked drainage paths and potential soil structure damage. Based on drainage anomalies, soil stability and compaction analysis are further performed to accurately assess the soil's anti-scour ability and sliding risk, and simulate the soil landslide process in advance. Combined with the terrain changes and vegetation distribution characteristics of the soil erosion area, the present invention identifies areas where ecological uplifts are formed and locates key areas with ecological restoration capabilities or terrain variations. Finally, the ecological uplift area and the soil landslide simulation results are integrated to comprehensively judge their stability levels, thereby achieving overall risk assessment and targeted early warning of the ecological slope protection, significantly improving the accuracy, systematicness and practicality of ecological slope protection monitoring.

[0010] Preferably, step S1 is specifically as follows:

[0011] Step S11: Acquire remote sensing data of the highway monitoring area;

[0012] Step S12: extracting vegetation index from remote sensing data of the highway monitoring area, and calculating vegetation coverage based on the vegetation index;

[0013] Step S13: identifying bare soil areas according to vegetation coverage;

[0014] Step S14: measuring the slope of the exposed soil area to obtain slope data; and calculating the high slope based on the slope data to obtain high slope data;

[0015] Step S15: performing soil degradation detection based on the exposed soil area to obtain soil degradation data;

[0016] Step S16: Identify the soil erosion area based on the high slope data and the soil degradation data to obtain soil erosion area data.

[0017] The present invention uses remote sensing data to rapidly cover and monitor large areas in real time, effectively breaking through the limitations of traditional monitoring in spatial scope and temporal frequency, and significantly improving information acquisition efficiency. The inversion calculation of vegetation coverage based on the vegetation index can comprehensively reflect the surface vegetation status, helping to accurately identify areas with sparse or degraded vegetation, and thus reflect the health of the ecosystem. The identified areas with exposed soil are high-risk areas for soil erosion. By conducting slope measurement and slope distribution statistics on these areas, the potential for gravitational erosion caused by the undulating terrain can be revealed, providing a key basis for subsequent erosion trend analysis. At the same time, soil degradation detection combined with sparse vegetation areas can not only identify degradation types such as salinization and drought, but can also be integrated with slope data to accurately define complex risk areas with high erosion potential and weak ecological functions. Finally, through the cross-identification of high slope information and soil degradation status, accurate labeling of soil erosion areas can be achieved, providing scientific, comprehensive, and real-time data support for subsequent ecological slope protection stability modeling, risk warning, and protection optimization, thereby effectively improving the automation level, coverage breadth, and analysis depth of the monitoring system.

[0018] Preferably, step S15 is specifically as follows:

[0019] Step S151: collecting remote sensing images of the bare soil area based on the bare soil area;

[0020] Step S152: dividing the band types according to the remote sensing image of the exposed soil area to obtain shortwave infrared band data and near infrared band data;

[0021] Step S153: calculating the salinization index based on the short-wave infrared band data and the near-infrared band data;

[0022] Step S154: Calculating shortwave infrared band reflectivity based on shortwave infrared band data;

[0023] Step S155: performing soil moisture inversion based on the shortwave infrared band reflectivity to obtain soil moisture data;

[0024] Step S156: soil degradation is determined based on the salinization index and soil moisture data to obtain soil degradation data.

[0025] The present invention can achieve high-resolution surface feature extraction in key areas through targeted collection of remote sensing images of exposed soil areas, ensuring that subsequent analysis is accurate and targeted; the band type division enables the effective separation of short-wave infrared and near-infrared band data, which helps to highlight the spectral response characteristics of soil salinity and moisture content and improve the resolution of soil property identification; the salinization index calculated based on short-wave infrared and near-infrared bands can reflect the degree of surface salinization and provide a reliable indicator for judging soil chemical degradation; through quantitative statistics of short-wave infrared band reflectivity, drought stress areas can be identified and soil moisture can be accurately inverted. The distribution of soil moisture and water content can be analyzed to reveal the impact of water loss on soil structure and vegetation growth. Combining the salinization index with humidity data to determine soil degradation can achieve a comprehensive diagnosis of physical, hydrological and chemical degradation, effectively making up for the shortcomings of traditional methods in incomplete identification of soil degradation types and single indicators. The entire process fully utilizes the spectral information characteristics and spatial coverage advantages of remote sensing data to achieve rapid identification, automatic extraction and risk assessment of soil degradation areas, providing a multi-source fusion and data-driven scientific basis for ecological slope protection stability analysis, greatly enhancing the system's perception of ecological degradation evolution trends and early warning effects.

[0026] Preferably, step S2 is specifically as follows:

[0027] Step S21: performing rainwater erosion simulation based on the soil erosion area data to obtain rainwater erosion data;

[0028] Step S22: collecting soil samples based on rainwater erosion data, and determining the looseness of the soil structure of the soil samples;

[0029] Step S23: estimating the anchoring strength of vegetation roots based on the looseness of the soil structure;

[0030] Step S24: determining the degree of vegetation root breakage based on the anchoring strength of the vegetation root system;

[0031] Step S25: determining the erosion intensity according to the degree of vegetation root breakage and the looseness of the soil structure to obtain erosion intensity data;

[0032] Step S26: extracting vegetation coverage data from the soil erosion area data;

[0033] Step S27: Perform drainage blockage analysis based on the vegetation coverage data and the erosion intensity data to obtain drainage blockage data.

[0034] The present invention organically integrates the dynamic simulation of soil erosion, soil physical structure assessment, vegetation root mechanical analysis and drainage path identification, thus significantly improving the accuracy and systematicness of the identification of ecological slope degradation process and hidden danger sources, and has the following beneficial effects: by constructing a rainwater erosion simulation model, the impact of rainfall on soil disturbance and migration under different slope structures can be quantitatively analyzed, revealing the potential distribution trend of erosion intensity; based on the simulation results, representative soil samples are collected and the structural looseness is measured, which helps to quantify the degree of change in the mechanical properties of the soil under the action of rainwater, which serves as an important physical basis for subsequent stability assessment; further, the anchoring capacity of vegetation roots is estimated based on the looseness of the soil structure, and the contribution of surface plants to slope stability can be deduced. The holistic approach uses a combination of soil structure and root stability to assess erosion intensity, helping to refine erosion risk levels and guide regional protection and reinforcement strategies. Combined with remotely sensed vegetation cover data, it assesses the integrity of slope ecological cover and the degree of functional decline. Finally, coupling erosion intensity with vegetation distribution to analyze drainage blockage can identify drainage path blockages caused by factors such as vegetation dieback and silt accumulation, thereby accurately pinpointing potential landslide and waterlogging hazards and providing scientific support for slope protection hydrological regulation and ecological restoration. This holistic approach transcends the limitations of traditional single monitoring indicators and dispersed data sources, enabling chained reasoning and monitoring of the entire process from rainwater disturbance to soil damage to vegetation instability to drainage anomalies. This provides a more comprehensive and efficient stability monitoring solution for ecological slope protection on mountainous highways.

[0035] Preferably, step S27 is specifically as follows:

[0036] Step S271: drawing a vegetation coverage area map based on the vegetation coverage data;

[0037] Step S272: extracting low vegetation coverage location information based on the vegetation coverage area map;

[0038] Step S273: performing leaf density detection based on the low coverage position information to obtain leaf density data; extracting leaf sparse data from the leaf density data;

[0039] Step S274: performing a maximum rainfall simulation based on the low coverage location information to obtain maximum rainfall data; determining the water absorption capacity of vegetation roots based on the maximum rainfall data to obtain vegetation root water absorption capacity data; performing a vegetation vitality analysis based on the vegetation root water absorption capacity data to obtain vegetation vitality data; and extracting vegetation low vitality data from the vegetation vitality data;

[0040] Step S275: determining the soil exposure area based on the leaf sparseness data and the vegetation low vitality data;

[0041] Step S276: Perform drainage blockage analysis based on the exposed soil area to obtain drainage blockage data.

[0042] The present invention strengthens the ability to accurately identify the decline of surface vegetation on ecological slope protection and the resulting drainage anomalies through in-depth monitoring of vegetation coverage quality and physiological activity status, and has the following beneficial effects: by drawing a vegetation coverage area map, the spatial distribution of slope vegetation can be visualized, providing a basis for subsequent positioning of risk areas; extracting low coverage locations can quickly focus on key areas with ecological degradation or human damage; detecting leaf density in these areas and obtaining sparse conditions can help reveal ecological degradation signals such as poor surface vegetation growth and insufficient photosynthesis; combined with maximum rainfall simulation, it can evaluate the The ability of vegetation to absorb water under extreme hydrological conditions is measured, thereby quantifying the role of its root system in regulating slope water. A vegetation vitality analysis mechanism based on this approach can accurately identify vegetation populations with declining physiological functions and weakened resilience, allowing for early detection of degradation risks. By combining leaf sparseness with low vitality data to derive exposed soil area, the extent of slopes without plant protection can be quantified, further clarifying their susceptibility to rainfall runoff erosion. Finally, drainage blockage analysis based on exposed area not only improves the ability to predict stormwater runoff blockage trends but also provides dynamic feedback on whether the drainage system's operating status is affected by ecological factors. The overall solution achieves a closed-loop perception and reasoning process from vegetation spatial distribution, morphological structure, physiological state, functional degradation, to drainage impacts, effectively addressing the blind spots of traditional methods in coupled monitoring of vegetation degradation and drainage blockage, and improving the comprehensiveness, accuracy, and timeliness of ecological slope protection system stability monitoring.

[0043] Preferably, step S276 is specifically as follows:

[0044] Soil wind erosion simulation is performed based on the soil exposure area to obtain soil wind erosion data;

[0045] Detect soil moisture based on soil wind erosion data to obtain soil moisture data;

[0046] Wet soil is identified based on soil moisture data to obtain wet soil data;

[0047] Identify coarse-grained soil based on soil wind erosion data to obtain coarse-grained soil data;

[0048] Perform soil transport simulation based on moist soil data and coarse-grained soil data to obtain soil transport data;

[0049] Obtain highway drainage paths; identify curved drainage paths;

[0050] Drainage blockage analysis is performed based on the curved drainage path and soil transport data to obtain drainage blockage data.

[0051] The present invention enhances the coupled perception capability of drainage patency of ecological slope protection and evolution trend of surface soil by introducing simulation analysis of soil wind erosion and transportation process, and has the following beneficial effects: by carrying out wind erosion simulation on soil exposed areas, it can quantitatively evaluate the migration trend of surface soil particles under wind action, and provide support for judging terrain evolution and stability changes under drought conditions; further inversion of soil moisture through remote sensing characteristics of wind erosion areas helps to identify the risk of increased soil drought induced by wind erosion, and improves the perception accuracy of local instability caused by uneven spatial distribution of moisture; on this basis, the identification of moist soil can effectively distinguish between soil types with cohesion and easy flow, and provide a basis for judging the starting point of particle movement for subsequent transportation path analysis; at the same time, the extraction of coarse-grained soil can improve the accuracy of transported material particles The ability to identify scales can improve the simulation accuracy of transportation paths; the intersection analysis of wet areas and coarse particle areas not only enhances the ability to determine transportation potential areas, but also improves the quantification accuracy of runoff particle carrying capacity; further combined with highway drainage paths, especially coupled simulation in curved drainage sections, can accurately determine the sedimentation risk of transported materials in areas with changing flow directions, especially kinetic energy attenuation and deposition-prone points in curved paths; by collaboratively analyzing particle migration trends and path geometric characteristics, the ability to identify hidden blockage points in the drainage system is improved, effectively expanding the continuity perception dimension of ecological slope protection monitoring from the "slope-pipeline" coupling perspective, providing a scientific early warning basis for early intervention in drainage anomalies, and thus providing more accurate and dynamic technical support in ensuring the stability of ecological slope protection.

[0052] Preferably, step S3 is specifically as follows:

[0053] Step S31: determining the drainage retention area based on the drainage blockage data;

[0054] Step S32: performing soil moisture evaporation analysis on the drainage retention area to obtain soil moisture evaporation data;

[0055] Step S33: Divide the soil layers based on the soil moisture evaporation data to obtain upper soil layer data and lower soil layer data;

[0056] Step S34: calculating the upper soil moisture according to the upper soil data to obtain the upper soil moisture; calculating the lower soil moisture according to the lower soil data to obtain the lower soil moisture; and calculating the humidity gradient according to the upper soil moisture and the lower soil moisture to obtain the humidity gradient data.

[0057] Step S35: determining soil moisture tension based on the moisture gradient data, simulating soil surface crack growth based on the soil moisture tension, and obtaining soil surface crack data; evaluating soil stability based on the soil surface crack data, and obtaining soil stability data;

[0058] Step S36: detecting soil compaction according to soil stability data;

[0059] Step S37: Perform soil landslide simulation based on soil compaction to obtain soil landslide data.

[0060] By acquiring drainage blockage data and determining drainage retention areas, the present invention can accurately identify areas that cause soil moistening and water accumulation, providing a basis for subsequent soil moisture evaporation analysis. Soil moisture evaporation analysis helps to more accurately understand the dynamic changes in soil moisture, thereby revealing the existence of moist areas and evaluating the permeability and water retention capacity of the soil. Soil layer division based on evaporation data can deeply analyze the moisture conditions of the upper and lower soil layers, which provides an important basis for subsequent moisture gradient analysis. Moisture gradient data can further reveal the moisture tension and stress distribution of the soil, providing scientific data support for soil crack growth simulation, thereby making a more accurate assessment of soil stability. By simulating cracks on the soil surface, the performance of the soil under different environmental conditions can be accurately predicted, and based on this, the stability of the soil can be evaluated and potential risks can be identified in advance. The correlation between soil stability data and soil compaction helps to identify stability problems caused by loose or over-compacted soil, thereby providing effective data support for the prevention of disasters such as landslides. Ultimately, soil landslide simulations can assess landslide risks under different soil conditions, optimize slope protection design and risk management, provide early warnings, and implement protective measures to ensure the safe and stable operation of expressways. These methods, through the multi-source integration of remote sensing data and ecological characteristics, provide comprehensive, accurate, and real-time data support for slope stability monitoring, overcoming the shortcomings of traditional monitoring methods and significantly improving the accuracy and timeliness of early warnings.

[0061] Preferably, step S36 is specifically as follows:

[0062] Step S361: Analyzing the erosion risk based on soil stability data to obtain soil erosion risk;

[0063] Step S362: predicting soil loss rate based on soil erosion risk;

[0064] Step S363: determining the amount of remaining soil according to the soil loss rate;

[0065] Step S364: Evaluate the soil compaction degree based on the soil remaining amount.

[0066] The present invention can identify areas where soil is susceptible to erosion in advance by performing erosion risk analysis based on soil stability data, providing key data support for subsequent management and protection measures. Soil erosion risk analysis helps predict erosion hotspots and provides a scientific basis for designing effective protection plans and controlling soil loss. By predicting the soil loss rate based on soil erosion risk, the speed of soil loss can be quantified and its long-term impact on the ecological environment can be assessed. Through the loss rate prediction, the remaining soil volume can be determined more accurately, providing decision makers with a basis for whether soil remediation or enhanced protection is needed, ensuring that the ecological function of the slope protection is not weakened. In addition, through the assessment of the remaining soil volume, combined with the soil compaction analysis, it is possible to reveal the stability problems faced by the soil after compaction, and help to effectively manage and optimize the slope protection system. This series of steps utilizes the combination of remote sensing technology and soil dynamic analysis to greatly improve the accuracy and effectiveness of highway ecological slope protection monitoring, and can identify risks in advance, optimize treatment plans and achieve scientific management, ensuring the long-term stability of the highway ecological environment.

[0067] Preferably, step S4 is specifically as follows:

[0068] Step S41: marking the soil erosion boundary according to the soil erosion area data;

[0069] Step S42: extracting terrain relief data based on the soil erosion area data;

[0070] Step S43: Calculating the slope change rate based on the terrain relief data;

[0071] Step S44: detecting the surface uplift area of ​​the soil erosion boundary according to the slope change rate to obtain surface uplift area data;

[0072] Step S45: detecting vegetation coverage based on the soil and water loss area data; identifying high vegetation coverage areas based on the vegetation coverage, and obtaining high vegetation coverage area data;

[0073] Step S46: performing a regional intersection operation based on the surface uplift area data and the high vegetation coverage area data to obtain ecological slope protection data;

[0074] Step S47: performing ecological slope protection stability analysis based on the ecological slope protection uplift data and the soil landslide data to obtain ecological slope protection stability data.

[0075] By marking the boundaries of soil erosion, the present invention helps accurately delineate areas susceptible to soil erosion, providing a precise basis for subsequent soil protection and restoration. Extracting terrain undulation data and calculating the slope change rate can help identify areas with larger slopes and predict areas with more severe soil erosion, allowing for more targeted monitoring and intervention. By detecting surface uplift areas at the boundaries of soil erosion, it is possible to identify areas with landslides or unstable soil, providing data support for strengthening ecological slope protection measures in these areas. At the same time, combined with the detection of vegetation cover, it helps to identify areas with rich vegetation, which can provide better natural barriers for soil and water conservation and reduce the risk of soil erosion. By performing an intersection operation on areas with high vegetation coverage and surface uplift areas, the most critical stable areas in the ecological slope protection system can be further identified, allowing resources to be concentrated for key protection. Finally, combining soil landslide data with ecological slope protection stability analysis can detect potential hidden dangers in the slope protection system in advance, predict its stability change trends, and provide a scientific basis for taking protective measures in advance. Overall, the use of these multi-angle, multi-data source analysis methods can comprehensively improve the monitoring accuracy and early warning capabilities of ecological slope protection, avoid the limitations of a single monitoring method, and provide more solid technical support for the ecological security of mountain highways.

[0076] Preferably, step S47 is specifically as follows:

[0077] Step S471: performing landslide impact force analysis based on soil landslide data to obtain landslide impact force data;

[0078] Step S472: Calculating the sliding path length based on the landslide impact force data to obtain soil landslide length data;

[0079] Step S473: Identify the impact area of ​​the ecological slope protection data based on the soil landslide length data to obtain the landslide impact data of the ecological slope protection data;

[0080] Step S474: Evaluate the stability of the ecological slope protection based on the ecological slope protection data and the landslide impact data to obtain ecological slope protection stability data.

[0081] The present invention helps to accurately assess the potential destructive power of landslides on slope protection systems through landslide impact force analysis, predict the impact of landslides, and help prepare protective measures in advance. Calculating the sliding path length based on landslide impact force data can provide detailed data on the range of landslide activity, allowing monitoring personnel to accurately understand the areas affected by the landslide and the extent of its impact. By identifying the impact area of ​​ecological slope protection data based on landslide length data, the specific threat posed by the landslide path to the ecological slope protection can be discovered, and it can be determined which areas require special attention or reinforcement. Finally, the stability of the ecological slope protection is evaluated in combination with landslide impact data, providing a scientific basis for decision makers to adjust protective measures in a timely manner and avoid potential ecological safety issues. This series of steps can effectively improve the stability of the ecological slope protection system through quantitative analysis and dynamic monitoring, avoid the blind spots and limitations of traditional monitoring methods, and provide solid technical support for the long-term safe operation of mountain highways. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0083] Figure 1 This is a schematic flow chart of the steps of a method for monitoring the stability of ecological slope protection on a highway according to the present invention;

[0084] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0085] Figure 3 Detailed step flow diagram of step S15 in the present invention;

[0086] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0087] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0088] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0089] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0090] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for monitoring the stability of an ecological slope protection of a highway, the method comprising the following steps:

[0091] Step S1: Acquire remote sensing data of the highway monitoring area; perform soil erosion area detection based on the remote sensing data of the highway monitoring area to obtain soil erosion area data;

[0092] In this embodiment, high-resolution image data is obtained from a satellite remote sensing system. These data include information from different spectral bands, such as visible light, near infrared, and short-wave infrared. In order to ensure the accuracy of the data, preprocessing is required, including radiation and geometric correction. Then, the vegetation coverage of the surface is evaluated by analyzing the vegetation index (such as NDVI) in the remote sensing image. By setting appropriate thresholds, different surface types can be divided, such as high vegetation coverage areas and bare surface areas. Combined with terrain information (such as slope data), the risk of soil and water loss in the monitoring area is evaluated. In this process, areas with large slopes and sparse vegetation coverage are identified, which are usually high-risk areas for soil and water loss. By combining these remote sensing images with terrain data, accurate soil and water loss area data is eventually obtained.

[0093] Step S2: performing erosion intensity analysis based on the soil erosion area data to obtain erosion intensity data; extracting vegetation coverage data from the soil erosion area data; performing drainage blockage analysis based on the vegetation coverage data and erosion intensity data to obtain drainage blockage data;

[0094] In this embodiment, the slope and vegetation coverage of each area are analyzed based on the soil erosion area data. The slope data comes from the digital elevation model (DEM), while the vegetation coverage is estimated by the NDVI value in the remote sensing image. Based on the combination of slope and vegetation coverage, the erosion intensity of each area is determined. The erosion intensity of a region can be estimated based on the known topography and surface cover information. Areas with higher erosion intensity often have larger slopes and less vegetation cover. On this basis, it is also necessary to analyze drainage blockage. Using precipitation data, topographic data and soil type data, the path of water flow after precipitation is simulated, and combined with the permeability of the soil to assess which areas are prone to poor drainage or blockage. This analysis helps to identify areas that cause soil erosion and further soil erosion, and then obtain drainage blockage data.

[0095] Step S3: performing soil stability analysis based on the drainage blockage data to obtain soil stability data; detecting soil compaction based on the soil stability data; performing soil landslide simulation based on the soil compaction to obtain soil landslide data;

[0096] In this embodiment, areas prone to soil erosion are identified based on drainage blockage data, and the shear strength of the soil is estimated. The calculation of soil shear strength usually needs to consider parameters such as soil moisture, density, and pressure. The moisture data can be obtained through soil moisture sensors or remote sensing data. Then, by detecting the compaction degree of the soil, the compactness of the soil can be understood. This data is crucial for landslide risk assessment. The measurement of soil compaction can be evaluated through field surveys using the porosity, density, and water content of the soil. Finally, based on these data, soil landslides in the area are predicted through a landslide simulation model. The landslide simulation takes into account the stability of the soil under conditions of different slopes, moisture, and soil compaction, thereby obtaining a risk assessment of landslide occurrence.

[0097] Step S4: Identify ecological slope protection areas based on the soil and water loss area data to obtain ecological slope protection data; perform ecological slope protection stability analysis based on the ecological slope protection data and soil landslide data to obtain ecological slope protection stability data.

[0098] In this embodiment, by analyzing the data of the soil erosion area, the area that needs ecological slope protection is identified. This process requires combining factors such as the slope of the area, vegetation coverage and soil stability to select areas suitable for ecological slope protection. Then, based on the data of these ecological slope protection areas and combined with the soil landslide data, an ecological slope protection stability analysis is performed. This analysis takes into account factors such as soil stability, vegetation coverage, and terrain characteristics to assess the stability of the ecological slope protection under current conditions. If the slope protection area is affected by a landslide, it is necessary to further evaluate whether it can effectively resist the threat of the landslide and then decide whether additional protective measures are needed. This process derives the overall stability data of the slope protection by analyzing the risk factors of different areas and the anti-erosion capacity of the slope protection area.

[0099] Preferably, step S1 is specifically as follows:

[0100] Step S11: Acquire remote sensing data of the highway monitoring area;

[0101] In the present embodiment, the image data of the highway monitoring area is obtained by remote sensing satellite or unmanned aerial vehicle platform. These image data should include multi-band information, especially the data of red light, near infrared and short wave infrared bands. The resolution of the remote sensing image needs to be at least 10 meters to ensure that the details in the area can be clearly captured, especially in areas with complex terrain and different vegetation coverage. After obtaining the data, it is necessary to carry out radiation correction to eliminate atmospheric effects and improve image quality to ensure that the data is accurate. Subsequently, the image obtained is geometrically corrected to adjust the geographic coordinates of the image to ensure the consistency of the remote sensing data with the actual geographic information. In this step, the remote sensing technology used is optical remote sensing technology, particularly the NDVI (normalized difference vegetation index) band combination for vegetation identification. The remote sensing data obtained can be stored, processed and subsequently analyzed by a geographic information system (GIS).

[0102] Step S12: extracting vegetation index from remote sensing data of the highway monitoring area, and calculating vegetation coverage based on the vegetation index;

[0103] In this embodiment, the acquired remote sensing image data is used to calculate the vegetation index (NDVI). NDVI is an index that measures the surface vegetation coverage by using the difference in reflectance between the near-infrared band and the red light band. The specific calculation method is: NDVI = (NIR-Red) / (NIR+Red), where NIR is the reflectance of the near-infrared band and Red is the reflectance of the red light band. According to the value of NDVI, the surface can be divided into different levels of vegetation coverage. Generally speaking, an NDVI value close to 1 indicates that the surface is covered with rich vegetation, while a value close to 0 or a negative value indicates that the area is bare land or water. By setting a threshold value of NDVI value (such as 0.2 or 0.3), the vegetation-covered area can be distinguished from the bare soil area, and then the vegetation coverage can be calculated. The calculation results of vegetation coverage can be used as basic data for subsequent analysis of bare soil areas and soil erosion.

[0104] Step S13: identifying bare soil areas according to vegetation coverage;

[0105] In this embodiment, a threshold for vegetation coverage is set (e.g., coverage below 20% is considered bare soil). Each pixel is then evaluated. If the vegetation coverage of a pixel falls below the threshold, the area is identified as bare soil. This process, combined with the spatial resolution of the remote sensing imagery, allows for pixel-by-pixel processing to accurately identify each bare soil area. The results of identifying bare soil areas provide foundational data for subsequent slope measurement, soil degradation analysis, and identification of soil erosion areas.

[0106] Step S14: measuring the slope of the exposed soil area to obtain slope data; and calculating the high slope based on the slope data to obtain high slope data;

[0107] In the present embodiment, digital elevation model (DEM) data is utilized to perform slope measurement in combination with the spatial information of the exposed soil area. The digital elevation model is obtained through remote sensing technology or ground measurement technology and can provide elevation information of the surface within the region. First, based on the DEM data, by calculating the elevation difference between adjacent pixels, a standard slope calculation method (such as a slope algorithm) is used to obtain the slope value of each pixel in the region. The calculation result of the slope is usually expressed in degrees or percentage. Then, by setting a slope threshold (such as a slope greater than 25 degrees), high slope areas can be counted, which are usually high-risk areas for soil erosion or landslides. Through these data, data of the high slope areas are further extracted to provide support for subsequent soil erosion area identification and soil stability analysis.

[0108] Step S15: performing soil degradation detection based on the exposed soil area to obtain soil degradation data;

[0109] In this embodiment, the determination of soil degradation is usually based on a comprehensive assessment of factors such as surface cover conditions, soil type, historical climate conditions, and the degree of soil erosion. By analyzing information such as the vegetation index change trend, soil moisture changes, and surface characteristics (such as the degree of exposure of exposed soil) in the exposed soil area, degradation assessment standards (such as a decrease in vegetation cover for two or more consecutive years) are used to determine whether the soil has degraded. In addition, the degree of soil degradation can be analyzed by combining changes in soil reflectivity in remote sensing images with historical meteorological data. By calculating indicators such as the vegetation restoration index (VRI) and the soil moisture index, the degree of soil degradation can be further determined, and the results can be stored in the form of a data set for subsequent analysis.

[0110] Step S16: Identify the soil erosion area based on the high slope data and the soil degradation data to obtain soil erosion area data.

[0111] In this embodiment, the main characteristics of soil erosion are areas with large slopes, low vegetation cover, and severe soil degradation. Therefore, by performing spatial analysis on the intersection of high-slope areas and soil-degraded areas, high-risk areas for soil erosion can be accurately identified. The specific operation is as follows: In the GIS platform, high-slope data and soil degradation data are first superimposed, and an intersection operation is performed based on the set threshold to obtain the soil erosion area. At this time, a weighted assessment is performed using terrain factors, vegetation factors, and soil factors to determine which areas have a higher risk of soil erosion, and ultimately obtain accurate soil erosion area data. These data provide a scientific basis for subsequent slope protection design, soil protection, and ecological restoration measures.

[0112] Preferably, step S15 is specifically as follows:

[0113] Step S151: collecting remote sensing images of the bare soil area based on the bare soil area;

[0114] In the present embodiment, it is necessary to select a suitable remote sensing platform for image acquisition. Commonly used remote sensing platforms include satellite platforms (such as Landsat 8, Sentinel-2) or unmanned aerial vehicle platforms. The remote sensing image needs to cover the soil exposed area to ensure that the image data has a high spatial resolution, and a spatial resolution of 10 meters or higher is usually selected. The collected image data should contain multi-band information, particularly red light, near infrared and short-wave infrared bands, because these bands are more sensitive to the identification of soil and vegetation. After acquiring the image, radiation correction is required to remove the influence of factors such as atmosphere and sun angle to ensure the accuracy of the image data. This process can use a radiation correction algorithm, such as the COSP (Cosine-Law) method, to further improve the quality of the image. Then, a geographic information system (GIS) is used to georeference the collected remote sensing image to ensure that the data of the remote sensing image is fully aligned with the actual geographical location, ultimately forming a remote sensing image of the soil exposed area that can be used for further analysis.

[0115] Step S152: dividing the band types according to the remote sensing image of the exposed soil area to obtain shortwave infrared band data and near infrared band data;

[0116] In this embodiment, band extraction is performed on the remote sensing image obtained in step S151 to separate different band data, especially the short-wave infrared band (SWIR) and the near-infrared band (NIR). The wavelength range of the short-wave infrared band is generally between 1.1μm and 3μm, while the wavelength range of the near-infrared band is from 0.7μm to 1.1μm. Each band in the remote sensing image corresponds to a specific spectral reflectance characteristic. By extracting the data of these two key bands, the moisture and salt content of the soil and vegetation and other characteristics of the surface material can be accurately captured. Specifically, using the band data of the remote sensing image, the reflectivity of each pixel in the short-wave infrared and near-infrared bands can be extracted, and these reflectivity values ​​will serve as the basis for subsequent analysis. Using these data, the salinization characteristics of the soil and soil moisture and other information can be further analyzed.

[0117] Step S153: calculating the salinization index based on the short-wave infrared band data and the near-infrared band data;

[0118] In this embodiment, the salinization index is usually used to measure the salt and alkaline components in the soil through a certain index or ratio. Commonly used salinization indices include the normalized difference salinization index (NDSI). The calculation method is to use the reflectance values ​​of the short-wave infrared band and the near-infrared band to calculate the salinization index through a specific formula. Specifically, the calculation formula of NDSI is: NDSI = (SWIR-NIR) / (SWIR+NIR), where SWIR is the reflectance of the short-wave infrared band and NIR is the reflectance of the near-infrared band. The calculation result of the salinization index will provide an indication of whether the soil is affected by salinization. Generally, areas with higher salinization index values ​​indicate that the soil is more affected by salt and alkali, while areas with lower index values ​​indicate that the soil is healthier. Through this method, areas affected by salt and alkali can be effectively identified, and data support can be provided for subsequent soil degradation judgments.

[0119] Step S154: Calculating shortwave infrared band reflectivity based on shortwave infrared band data;

[0120] In this embodiment, the reflectivity data of the short-wave infrared band is extracted from the remote sensing image, and the reflectivity value of each pixel can be directly obtained through the remote sensing platform. Then, the reflectivity of the short-wave infrared band is statistically processed to calculate statistical indicators such as the average reflectivity and standard deviation of all pixels in the area. The reflectivity value of the short-wave infrared band is closely related to the moisture content of the soil. A higher reflectivity usually means that the soil is drier, while a lower reflectivity means that the soil contains more water. Based on the statistical results, the wetness of the soil can be further analyzed to provide a data basis for the inversion of soil moisture. In addition, these reflectivity data can also be used to analyze the changes in soil moisture content and monitor the changing trend of soil moisture.

[0121] Step S155: performing soil moisture inversion based on the shortwave infrared band reflectivity to obtain soil moisture data;

[0122] In this embodiment, there is a certain negative correlation between the moisture of the soil and the reflectivity of the short-wave infrared band, that is, the higher the soil moisture, the lower the reflectivity of the short-wave infrared band. Using this relationship, the reflectivity of the short-wave infrared band can be converted into a soil moisture value through an empirical formula or a regression model. Specifically, a regression model based on the relationship between reflectivity and soil moisture can be established, and experimental data is usually used to calibrate the model. During the inversion process, regression parameters under specific soil types and environmental conditions can be selected to improve the inversion accuracy. The soil moisture data obtained by inversion can help analyze the moisture status of the soil.

[0123] Step S156: soil degradation is determined based on the salinization index and soil moisture data to obtain soil degradation data.

[0124] In this embodiment, soil degradation is usually caused by the combined effects of factors such as salinization and lack of water. Therefore, the salinization index and soil moisture are key indicators for assessing soil degradation. First, the threshold of the salinization index is set (such as NDSI greater than 0.25 indicates a salinized area) to identify whether the soil is affected by salinization. Then, based on the inversion results of soil moisture, a humidity threshold is set (such as humidity below 15% indicates dry soil), and combined with the salinization condition of the soil, it is determined whether there is a soil degradation problem. By combining the salinization index and soil moisture data, soil degraded areas can be accurately identified and marked as degraded areas. These data provide a scientific basis for further ecological restoration measures, soil protection and governance.

[0125] Preferably, step S2 is specifically as follows:

[0126] Step S21: performing rainwater erosion simulation based on the soil erosion area data to obtain rainwater erosion data;

[0127] In this embodiment, remote sensing data of soil erosion areas are used to perform rain erosion simulation, with the aim of calculating and analyzing soil erosion intensity under different precipitation conditions. Rain erosion simulation usually uses physical hydrological models, such as flow models (e.g., SWAT models) or soil erosion models (e.g., USLE models). First, precipitation data related to the study area is obtained, which are usually obtained through meteorological stations or remote sensing data. During the simulation, the erosion intensity of different areas needs to be calculated based on factors such as precipitation intensity, precipitation duration, and slope. For example, in areas with large precipitation and steep slopes, the rain erosion intensity will be greater. For the acquisition of slope data, high-precision DEM (digital elevation model) data is used for analysis, and further combined with the influence of soil type and vegetation coverage, simulation calculations are performed. The results of the erosion simulation are output in the form of a raster layer, and each pixel value represents the erosion intensity of the area.

[0128] Step S22: collecting soil samples based on rainwater erosion data, and determining the looseness of the soil structure of the soil samples;

[0129] In this embodiment, according to the rainwater scour data obtained in step S21, a suitable location is selected in the soil erosion area to collect soil samples. The sampling location should be determined according to the scour intensity of the simulation results, and areas with greater scour intensity are preferably selected for sampling. Soil sample collection usually uses standard soil sampling tools to collect soil samples at different depths (such as 10 cm in the surface layer and 20-30 cm in the deep layer). The collected soil samples will be analyzed in the laboratory, mainly testing the particle size distribution, porosity, density, etc. of the soil to evaluate the looseness of the soil structure. Soil with higher looseness usually has larger particles and higher porosity, and is easily affected by rainwater scour. The quantitative determination of looseness can be achieved by soil compaction test or specific gravity analysis, and the laboratory data will provide specific looseness values.

[0130] Step S23: estimating the anchoring strength of vegetation roots based on the looseness of the soil structure;

[0131] In this embodiment, the anchoring strength of the vegetation roots is evaluated based on the looseness data of the soil structure. Areas with higher looseness of the soil structure usually have looser soil particles, and the anchoring effect of the vegetation roots is poor, which is easily affected by wind and rain and causes the roots to loosen. In this step, the data in the soil sample (such as soil porosity, density, etc.) is combined with the vegetation type and root distribution, and an empirical formula or an existing biomechanical model is used to calculate the anchoring force of the root system. The anchoring strength of the root system can be evaluated by analyzing the extension depth of the root system and the compactness of the soil. For example, roots in loose soil tend to grow shallowly, thereby reducing the root system's ability to anchor to the soil. Through this process, the anchoring strength data of the vegetation roots in the area can be obtained.

[0132] Step S24: determining the degree of vegetation root breakage based on the anchoring strength of the vegetation root system;

[0133] In this embodiment, the degree of root fracture is closely related to the looseness of the soil, the type of vegetation and its growth conditions. In this step, a certain critical value is set. When the root anchoring force is less than a certain threshold, the root system will be broken or loose. The threshold can be determined by experimental data or existing research results. For example, when the root anchoring force is lower than 0.1N / m, it is considered that the root system is at risk of fracture. By analyzing the anchoring force of vegetation roots under different soil conditions, combined with data such as root depth and soil moisture, the degree of root fracture in each area can be evaluated to obtain relevant data.

[0134] Step S25: determining the erosion intensity according to the degree of vegetation root breakage and the looseness of the soil structure to obtain erosion intensity data;

[0135] In this embodiment, the breakage of the vegetation roots causes the soil surface to lose stability, and the loose soil is easily washed away by water, leading to severe erosion. Therefore, the calculation of erosion intensity needs to combine these two factors. First, a weight model of soil looseness and root breakage degree is set, and an empirical formula for soil erosion intensity is established based on experimental data or existing literature. This formula usually takes soil looseness and root breakage degree as input factors and outputs the erosion intensity of the area. For example, areas with loose soil and broken roots will significantly increase the erosion intensity. By analyzing each area, a grid map representing different erosion intensities is generated, and each pixel represents the erosion intensity of the area.

[0136] Step S26: extracting vegetation coverage data from the soil erosion area data;

[0137] In this embodiment, vegetation coverage is a key factor affecting soil erosion and soil stability, so the data must be accurately obtained. The extraction of vegetation coverage can be achieved through NDVI (normalized vegetation index). The calculation of NDVI value is based on data from red light and near infrared bands, and the obtained NDVI value is usually between -1 and +1. An NDVI value above 0.3 represents good vegetation coverage, while an NDVI value below 0.1 indicates that the soil surface is exposed. Through remote sensing image processing, the vegetation coverage data of the area is extracted and the corresponding raster layer is generated.

[0138] Step S27: Perform drainage blockage analysis based on the vegetation coverage data and the erosion intensity data to obtain drainage blockage data.

[0139] In this embodiment, the occurrence of drainage blockage is usually closely related to vegetation coverage and erosion intensity. Areas with low vegetation coverage or high erosion intensity are prone to poor drainage, which further aggravates soil erosion. By analyzing vegetation coverage and erosion intensity, it is possible to determine which areas have poor drainage problems. First, by setting the standards of the drainage system, such as slope, drainage channel width, etc., combined with terrain data and water flow paths, drainage efficiency is evaluated. Then, based on vegetation coverage and erosion intensity data, potential drainage blockage areas are identified. These areas will be marked as drainage blockage risk areas and provided for subsequent repair and management plans.

[0140] Preferably, step S27 is specifically as follows:

[0141] Step S271: drawing a vegetation coverage area map based on the vegetation coverage data;

[0142] In the present embodiment, the vegetation coverage information of the ecological slope protection area of ​​the highway is obtained through remote sensing data or ground survey data. Vegetation coverage data is usually represented by NDVI (normalized difference vegetation index), and NDVI values ​​are usually calculated by red light band and near infrared band data in remote sensing images. The range of NDVI values ​​is -1 to +1, where high values ​​(such as 0.3 and above) represent good vegetation coverage, and low values ​​(such as below 0.1) represent bare soil or sparse vegetation. According to these NDVI data, a vegetation coverage area map is generated using GIS software (such as ArcGIS or QGIS), which divides the entire study area into different areas according to vegetation coverage. High coverage areas are identified as well-vegetated areas, and low coverage areas are identified as sparsely vegetationed areas or bare soil areas. The map generated by this process provides basic data for vegetation coverage analysis and vegetation restoration in subsequent steps.

[0143] Step S272: extracting low vegetation coverage location information based on the vegetation coverage area map;

[0144] In this embodiment, the area with poor vegetation coverage is identified by setting an NDVI threshold (for example, NDVI is less than 0.2). The entire area is rasterized using a GIS tool, and areas below the set threshold are screened out based on the NDVI value. The selected low coverage areas usually correspond to areas with sparse vegetation or exposed soil, representing that these areas need to be given priority in the ecological restoration process. Using the rasterized vegetation coverage map, the location of the low coverage area can be accurately demarcated, and geocoding can be performed to generate corresponding location information data. These location information provides data support for subsequent steps such as vegetation density detection and rainfall simulation.

[0145] Step S273: performing leaf density detection based on the low coverage position information to obtain leaf density data; extracting leaf sparse data from the leaf density data;

[0146] In this embodiment, the vegetation data obtained through remote sensing images uses high-resolution remote sensing image analysis technology, such as hyperspectral remote sensing or multispectral remote sensing, to detect the distribution of vegetation leaves. The green light band and near-infrared band in the remote sensing image are used to calculate the leaf density of the vegetation. The higher the leaf density, the more luxuriant the vegetation growth. The leaf density can be further inferred by the quantitative NDVI value, and the NDVI value is usually correlated with the leaf density. For low coverage areas, a threshold is set (such as NDVI less than 0.2) to distinguish areas with low leaf density. After extracting the leaf density data, the leaf sparseness of these areas is calculated. If the leaf density is less than a certain standard (such as the leaf density is less than 50%), it can be marked as a leaf sparse area. These leaf sparse data are helpful in the design of subsequent vegetation restoration and protection measures.

[0147] Step S274: performing a maximum rainfall simulation based on the low coverage location information to obtain maximum rainfall data; determining the water absorption capacity of vegetation roots based on the maximum rainfall data to obtain vegetation root water absorption capacity data; performing a vegetation vitality analysis based on the vegetation root water absorption capacity data to obtain vegetation vitality data; and extracting vegetation low vitality data from the vegetation vitality data;

[0148] In this embodiment, rainfall simulation is typically based on data from meteorological stations or historical rainfall records, combined with factors such as topography and vegetation cover, using hydrological models such as the SCS-CN (Soil Conservation Service Curve Number) model or a rainstorm simulation model to estimate rainfall intensity and amount. Based on the simulation results, maximum rainfall data for each region can be obtained. Subsequently, based on the maximum rainfall data and combined with soil and vegetation types, a biohydrological model (e.g., a root water absorption model) is used to assess the water absorption capacity of vegetation roots. The water absorption capacity of vegetation roots can be calculated based on the vegetation type, root depth, and soil moisture conditions. For example, deep-rooted plants generally have stronger water absorption capacity. Next, the water absorption capacity of vegetation is used to analyze vegetation vitality. Vegetation vitality can be determined by monitoring parameters such as leaf moisture status and photosynthetic efficiency. A vitality threshold is set (e.g., a leaf moisture content below 30% indicates low vitality). Based on this threshold, areas of low-vitality vegetation are identified for further analysis of their health.

[0149] Step S275: determining the soil exposure area based on the leaf sparseness data and the vegetation low vitality data;

[0150] In this embodiment, the soil exposure area is usually closely related to the sparseness of vegetation and the vitality of vegetation. By combining the leaf sparsity and vegetation vitality, the proportion of exposed soil can be calculated. If the area has high leaf sparsity and low vegetation vitality, its soil exposure area is larger. First, a soil exposure threshold is set by combining the leaf sparse data and the vegetation low vitality data obtained in step S273 and step S274. Based on this threshold, it can be determined which areas have a larger soil exposure area, and spatial analysis can be performed to obtain the specific areas of these areas. Use GIS analysis tools to extract the spatial information of these areas and generate a soil exposure map to provide data support for subsequent soil erosion prevention and control and ecological restoration.

[0151] Step S276: Perform drainage blockage analysis based on the exposed soil area to obtain drainage blockage data.

[0152] In this embodiment, the soil exposure area is usually accompanied by the problem of poor drainage, especially during rainfall, when water easily accumulates in these exposed soil areas and forms blockages. The situation of the drainage channel is evaluated by combining terrain data (such as slope, ground elevation, etc.) with soil exposure area data. A water flow simulation model (such as the HEC-RAS model) is used to analyze the water flow path in the soil exposure area after rainfall to predict the flow of water in the exposed soil area. By setting drainage system standards (such as maximum slope, drainage channel width, etc.) and combining soil exposure area data, areas with poor drainage or water accumulation are identified. The analysis results generate a drainage blockage risk map, mark areas with poor drainage, and provide a basis for subsequent drainage system optimization.

[0153] Preferably, step S276 is specifically as follows:

[0154] Soil wind erosion simulation is performed based on the soil exposure area to obtain soil wind erosion data;

[0155] In the present embodiment, according to the soil exposure area data, a wind erosion simulation model (such as Wind Erosion Prediction System, WEPS) is used to perform simulation analysis of wind erosion. The soil exposure area data is usually obtained by remote sensing images or ground field surveys to demarcate the area of ​​exposed soil. The input parameters of the model include soil type (such as sandy soil, clay, etc.), wind speed, soil moisture, soil particle size distribution, etc. By setting a soil wind erosion threshold (for example, when the wind speed is greater than a certain standard, or when the soil moisture content is lower than a certain value), it can be determined which areas are prone to wind erosion. In the simulation process, the intensity of wind erosion is usually expressed by the mass lost by the soil (such as tons / hectare). The wind erosion data obtained by simulation calculation can mark the wind erosion intensity distribution in the highway slope protection area. Wind erosion data provides an important basis for subsequent soil moisture analysis and soil remediation.

[0156] Detect soil moisture based on soil wind erosion data to obtain soil moisture data;

[0157] In this embodiment, the moisture condition of the soil when wind erosion occurs can be inferred through the relationship between the intensity of wind erosion and soil moisture. Specifically, low-humidity soil is more susceptible to wind erosion, so the soil moisture value is first determined using the NDVI of remote sensing images or a soil moisture model (such as a soil moisture inversion algorithm based on short-wave infrared band data). The unit of soil moisture data is usually a percentage (%), representing the volume content of water in the soil. By setting a threshold for soil moisture (such as humidity less than 15%), soil areas where wind erosion is exacerbated can be identified, so that precise soil remediation measures can be taken. These soil moisture data help understand the drought condition of the soil and its susceptibility to wind erosion.

[0158] Wet soil is identified based on soil moisture data to obtain wet soil data;

[0159] In this embodiment, moist soil generally has a higher moisture content and is not easily blown away by the wind when wind erosion occurs. First, a standard threshold for soil moisture is set (e.g., moisture content greater than 20% indicates moist soil). By comparing soil moisture data with the threshold, moist soil areas can be identified. The moisture in these moist soil areas is sufficient to increase the binding force between soil particles, thereby reducing the probability of wind erosion. Data on moist soil is generally generated based on a combination of remote sensing images, ground measurements, and wind erosion simulation models. The delineation results of moist soil areas can help further identify high-risk wind erosion areas and provide data support for soil protection and restoration.

[0160] Identify coarse-grained soil based on soil wind erosion data to obtain coarse-grained soil data;

[0161] In this embodiment, coarse-grained soil generally includes sand and gravel, etc. The particles of this type of soil are relatively large and are easily affected by wind and eroded. Based on the soil wind erosion simulation data, wind erosion intensity information of different regions can be obtained, and areas with higher wind erosion intensity are often composed of coarse-grained soil. Through soil particle size analysis, soil physical properties (such as particle diameter) are used for classification, and certain standards are set (such as particle diameter greater than 0.2mm is coarse-grained soil), coarse-grained soil in the highway slope protection area can be further identified. After identifying the coarse-grained soil, basic data can be provided for wind erosion prevention and control design, helping to reinforce or revegetate these areas.

[0162] Perform soil transport simulation based on moist soil data and coarse-grained soil data to obtain soil transport data;

[0163] In this embodiment, soil transport analysis is performed using a soil transport simulation model (such as the USLE model, the Wind Erosion Prediction System) based on moist soil data and coarse-grained soil data. By setting the input parameters (such as soil particle size, wind speed, humidity, etc.) in the soil transport model, the transport process of soil particles is simulated. The presence of moist soil can slow down the transport of soil, but in coarse-grained soil areas, wind erosion still occurs even if the soil is moist. Through soil transport simulation, the amount of soil transported (such as the amount of soil lost) is obtained. These soil transport data help understand the vulnerability of soil under different humidity and particle size conditions, and provide a basis for taking effective wind erosion protection measures.

[0164] Obtain highway drainage paths; identify curved drainage paths;

[0165] In this embodiment, GIS technology is used to obtain drainage path data along the highway. Drainage path data is usually extracted through a digital elevation model (DEM), and the drainage channel is determined by analyzing the slope of the terrain and the water flow path. Curved drainage paths usually appear in areas with complex terrain. These curved drainage paths can be identified by analyzing the slope changes on both sides of the road. Using GIS software (such as ArcGIS or QGIS), combined with slope, watershed analysis and other technologies, the drainage path is extracted and the location of the curved part is identified. This data will be used for subsequent drainage blockage analysis to help evaluate drainage efficiency and possible risks.

[0166] Drainage blockage analysis is performed based on the curved drainage path and soil transport data to obtain drainage blockage data.

[0167] In this embodiment, the drainage blockage analysis is to predict the flow of water in a curved drainage path by simulating rainfall or water flow processes. Since curved drainage paths usually cause water flow to be retained and decelerated, water will accumulate in these locations. By using a water flow model (such as HEC-RAS), combined with the spatial data of the drainage path and soil transport data, the water flow path and soil transport conditions under different rainfall intensities are analyzed. The simulation results can show the areas where water accumulation occurs in the drainage path and identify the weak links in the drainage system. Based on the simulation analysis, drainage blockage data is obtained, indicating which areas are at risk of poor drainage. These data help to optimize the design of the drainage system and prevent soil erosion and drainage system failures.

[0168] Preferably, step S3 is specifically as follows:

[0169] Step S31: determining the drainage retention area based on the drainage blockage data;

[0170] In this embodiment, the drainage blockage data is obtained through the aforementioned drainage path analysis, which describes the areas where water is retained in the drainage path. By performing a terrain analysis on the drainage path, the key points of water retention are identified, especially the areas with bends or gentle slopes. These areas are usually characterized by slow water flow or reverse flow of water. On this basis, the weak links of the drainage path are further clarified through terrain data, DEM (digital elevation model), slope analysis and other methods, and these areas are identified as drainage retention areas. This process can use GIS software (such as ArcGIS, QGIS, etc.) for spatial analysis to extract retained water areas and potential water accumulation areas. The data results are usually expressed in terms of the volume, area or retention time of the retained water body.

[0171] Step S32: performing soil moisture evaporation analysis on the drainage retention area to obtain soil moisture evaporation data;

[0172] In this embodiment, soil moisture evaporation is calculated by soil moisture data and meteorological data (such as temperature, humidity, wind speed, etc.). Water evaporation is mainly affected by factors such as climatic conditions, soil type and soil moisture. Common water evaporation formulas such as the Penman-Monteith evaporation model or the Hargreaves model are used to combine soil moisture and meteorological data to calculate the evaporation rate. The evaporation rate is usually expressed in millimeters per day, and spatial distribution analysis can be performed within the retention area to identify areas where water evaporation is more concentrated. Parameter settings such as the soil evaporation coefficient and the wind speed coefficient need to be calibrated using experimental data. The results of the evaporation data will help understand the moisture dynamics of the retention area and facilitate subsequent soil moisture management and optimization.

[0173] Step S33: Divide the soil layers based on the soil moisture evaporation data to obtain upper soil layer data and lower soil layer data;

[0174] In this embodiment, based on the soil moisture evaporation data, the soil is layered and processed to obtain upper soil data and lower soil data. First, based on the actual structure and evaporation characteristics of the soil, the soil is divided into an upper soil layer (usually 0-20 cm deep) and a lower soil layer (usually 20-60 cm deep). Soil at different levels behaves differently in terms of water evaporation, water absorption capacity and structure, and therefore needs to be processed separately. Using soil moisture sensors or remote sensing image data, combined with soil moisture evaporation data, the water evaporation rate and water storage capacity of the upper soil layer and the lower soil layer are calculated respectively. The upper soil data and the lower soil data respectively reflect the distribution of soil moisture, providing basic data for further moisture calculation and soil stability analysis.

[0175] Step S34: calculating the upper soil moisture according to the upper soil data to obtain the upper soil moisture; calculating the lower soil moisture according to the lower soil data to obtain the lower soil moisture; and calculating the humidity gradient according to the upper soil moisture and the lower soil moisture to obtain the humidity gradient data.

[0176] In this embodiment, the humidity of the upper soil layer (0-20 cm) and the lower soil layer (20-60 cm) is calculated separately. The humidity value can be obtained by direct measurement or by inversion using remote sensing data. Common soil moisture measurement methods include obtaining field humidity data through sensors (such as TDR, time domain reflectometry), or inverting soil humidity using short-wave infrared band data in remote sensing images. Using these data, the upper soil humidity and the lower soil humidity are calculated separately. Next, by comparing the difference in soil humidity between the upper and lower layers, the humidity gradient, that is, the uneven distribution of water between the surface and deep layers of the soil, is calculated. The humidity gradient is usually expressed as humidity difference (such as %), and its calculation method is humidity gradient = upper layer humidity - lower layer humidity. This data helps to evaluate the water conductivity of the soil and its water retention capacity under drought conditions.

[0177] Step S35: determining soil moisture tension based on the moisture gradient data, simulating soil surface crack growth based on the soil moisture tension, and obtaining soil surface crack data; evaluating soil stability based on the soil surface crack data, and obtaining soil stability data;

[0178] In this embodiment, water tension reflects the tensile strength of water on the soil surface, and is usually described using a soil water tension model (such as the Van Genuchten model). The water tension can be inferred from the humidity gradient data. Next, the soil water tension data is used to simulate the growth of cracks on the soil surface. The growth of soil cracks is usually caused by water loss. When the soil water evaporates, the surface water decreases, which causes cracks on the soil surface. The crack growth model (such as a tension-based fracture mechanics model) is used to simulate the cracks on the soil surface. The simulation results are expressed in terms of crack length and density. By analyzing the distribution and morphology of the cracks, the stability of the soil can be evaluated. The soil stability data reflects the soil's ability to resist cracking under stress and moisture changes, which in turn affects whether the soil is prone to disasters such as landslides.

[0179] Step S36: detecting soil compaction according to soil stability data;

[0180] In this embodiment, the compaction degree of the soil directly affects the structural stability of the soil. Excessive compaction will lead to poor soil aeration and prone to landslides and other problems. The compaction degree of the soil is calculated by detecting the dry density and wet density of the soil. The compaction degree of the soil is usually expressed by the compaction ratio (or relative density). The compaction degree is measured at different depths by sampling and analyzing soil samples. The density data of the soil sample can be obtained using a soil density meter (such as a nuclear density meter or a disc compactor). The threshold value of soil compaction degree is usually set at 0.8-1.2g / cm 3 Within this range, the soil is relatively loose and has good aeration; above this threshold, the soil has compaction problems.

[0181] Step S37: Perform soil landslide simulation based on soil compaction to obtain soil landslide data.

[0182] In this embodiment, the compaction degree of the soil is closely related to its anti-slip ability. By introducing soil mechanics models (such as landslide stability analysis models) and soil shear strength, friction angle and other parameters, the landslide risk of soil in different environments can be simulated. The soil landslide simulation process usually includes the influence of slope, soil structure and moisture, and simulates the critical conditions for landslide occurrence. Landslide data usually include parameters such as the probability of landslide occurrence, the volume of the landslide and the sliding distance of the landslide. These simulation data provide data support for landslide prevention and soil protection measures.

[0183] Preferably, step S36 is specifically as follows:

[0184] Step S361: Analyzing the erosion risk based on soil stability data to obtain soil erosion risk;

[0185] In this embodiment, it is necessary to collect soil stability data, including information such as soil structure, compaction, and moisture. These data can be obtained through soil sample testing and remote sensing data inversion. For soil stability data, key parameters include soil shear strength, tensile strength, compaction, and water tension. Based on these data, soil erosion risk can be assessed through a soil erosion model (such as a USLE model or a RUSLE model). The USLE model usually includes factors such as precipitation, soil type, topography, and vegetation coverage, while the RUSLE model further considers the erosive effect of water flow. In this model, the specific values ​​of parameters such as precipitation intensity, soil type, and slope need to be extracted from field measurements or remote sensing data. Through the combination of these parameters, soil erosion risk data is obtained, which is usually expressed as a soil erosion index or erosion grade. Specifically, terrain analysis tools (such as slope calculation) and vegetation coverage analysis can be used to provide input parameters for the soil erosion model. The soil erosion risk data finally generated will show which areas have high erosion risks, which is

[0186] Step S362: predicting soil loss rate based on soil erosion risk;

[0187] In this embodiment, soil loss rate refers to the amount of soil lost per unit area, usually in tons / hectare / year. When predicting the soil loss rate, a soil erosion model (such as the RUSLE model) is first used, which requires the input of the following parameters: precipitation, slope, soil type, vegetation coverage and human activities (such as farming and construction). Among them, precipitation data can be obtained through meteorological station data or satellite remote sensing data, slope data can be extracted through digital elevation model (DEM) analysis, and soil type and vegetation coverage can be obtained through field surveys or remote sensing image data. When predicting the loss rate, the erosive force of water flow must also be considered, especially in areas with faster flow rates after heavy rain. Once these parameters are obtained, the amount of soil loss in each area can be calculated and quantified by region. By analyzing the loss rate, it is determined which areas have more serious soil loss, providing support for subsequent repair and control measures.

[0188] Step S363: determining the remaining soil amount according to the soil loss rate;

[0189] In this embodiment, soil loss rate data is obtained from step S362, which reflects the soil loss situation in different areas and different time periods. The remaining soil in each area is calculated based on the loss rate, and the formula is: remaining soil = initial soil amount - lost soil amount. The initial soil amount can be estimated by measuring the soil depth and soil density. Specifically, the soil depth can be obtained through geological exploration data, and the soil density can be obtained through laboratory analysis or in-situ testing (such as using a nuclear density meter). For different soil types, the density value is different and needs to be calibrated according to the actual situation. The amount of lost soil is calculated by the soil loss rate, usually considering a specific time period (such as one year), and the total loss amount is obtained based on the loss amount per unit area. Through these data, the remaining soil amount can be estimated in different areas, and further identification can be made of areas where the soil has been severely eroded or lost.

[0190] Step S364: Evaluate the soil compaction degree based on the soil remaining amount.

[0191] In this embodiment, it is necessary to evaluate the soil compaction based on the data of soil residue, combined with the standard values ​​of soil density, moisture and compaction. The evaluation of soil compaction is usually carried out by measuring the relative density of the soil (such as the ratio of the dry density of the measured soil sample to the standard density). Density data of different soil layers can be obtained through laboratory methods (such as using a standard density instrument or gravity method). Further, the degree of soil compaction can be calculated by density change analysis, combined with factors such as soil moisture and temperature. Specifically, if the soil residue is low, it means that the soil in the area has suffered a large degree of erosion, resulting in changes in compaction, especially in areas with larger slopes. Use GIS tools to analyze the spatial distribution of soil residue, and combine the actual compaction of the soil to generate a soil compaction map to evaluate the impact of soil compaction on ecosystem stability. For areas with high compaction, loosening and restoration measures need to be taken to avoid further soil degradation.

[0192] Preferably, step S4 is specifically as follows:

[0193] Step S41: marking the soil erosion boundary according to the soil erosion area data;

[0194] In the present embodiment, it is necessary to collect data on the soil erosion area, which can be obtained through remote sensing images, field surveys or historical soil erosion monitoring data. The data on the soil erosion area include information such as precipitation, watershed area, terrain characteristics, soil type, etc. in the area. Through GIS (Geographic Information System) technology, these data are spatially analyzed, and the boundaries of the soil erosion area are marked in combination with data such as soil erosion rate, slope change, and vegetation coverage. When marking the boundary, it is first necessary to use a topographic map (such as DEM, digital elevation model) to analyze the terrain undulations of the area and calculate the degree of soil erosion in different areas. Through threshold setting, for example, an area where the soil erosion rate exceeds a certain value is selected as a soil erosion area, and then its boundary is demarcated. Through these data inputs, the specific boundaries of soil erosion are obtained, which are usually marked as polygonal areas. These data can provide reference for subsequent measures such as soil remediation and vegetation planting.

[0195] Step S42: extracting terrain relief data based on the soil erosion area data;

[0196] In this embodiment, terrain undulation refers to the degree of elevation of the earth's surface. This feature is crucial when analyzing soil erosion because areas with large slopes are prone to soil erosion. The extraction of terrain undulation data is usually obtained through a digital elevation model (DEM). DEM data can be obtained through remote sensing technology, satellite imagery, or laser radar scanning (LiDAR). By analyzing the DEM data, the altitude value of each pixel is extracted, and then the degree of terrain undulation is calculated based on the height difference between adjacent pixels. Commonly used algorithms include slope calculation and surface curvature calculation. Terrain undulation data provides the necessary spatial information for subsequent slope change rate calculation and prediction of soil erosion in the basin.

[0197] Step S43: Calculating the slope change rate based on the terrain relief data;

[0198] In this embodiment, the calculation of the slope change rate is usually performed using a digital elevation model (DEM), and the slope is calculated by using the elevation value of each pixel in the DEM data and the difference between the neighborhood pixels. Specifically, by calculating the slope of each pixel, the slope distribution of the entire area is obtained, and then the slope change rate is calculated based on the change in the slope between adjacent pixels. The calculation formula for the slope change rate is usually: slope change rate = (slope difference) / (distance). Areas with larger values ​​usually represent drastic slope changes and are sensitive areas for soil erosion. In this process, it is necessary to set appropriate thresholds based on the characteristics of the area. For example, areas where the slope change rate exceeds a certain value will be considered high-risk areas for soil erosion.

[0199] Step S44: detecting the surface uplift area of ​​the soil erosion boundary according to the slope change rate to obtain surface uplift area data;

[0200] In this embodiment, by analyzing the slope change rate, the boundary of the soil erosion area is further detected for surface uplift areas. Surface uplift areas refer to local uplift phenomena caused by geological effects, weathering, and other reasons. These areas are usually less susceptible to soil erosion. The detection process first requires the slope change rate data obtained in the previous steps to identify areas with drastic slope changes. These areas are usually related to surface uplift. By further analyzing the slope change rate, combined with data such as terrain undulation and soil type, it is determined which areas have uplift characteristics. The detection of surface uplift areas is usually carried out based on a certain slope change rate threshold. For example, when the slope change rate is greater than a certain set value, the area is regarded as a surface uplift area, thereby obtaining uplift area data. These data can help identify which areas are potential areas for soil and water conservation and provide a basis for ecological slope protection.

[0201] Step S45: detecting vegetation coverage based on the soil and water loss area data; identifying high vegetation coverage areas based on the vegetation coverage, and obtaining high vegetation coverage area data;

[0202] In this embodiment, the vegetation coverage can be obtained through remote sensing image analysis, and the NDVI (normalized vegetation index) value is usually used for estimation. The calculation formula of the NDVI value is: NDVI = (NIR-RED) / (NIR+RED), where NIR is the reflectivity of the near-infrared band and RED is the reflectivity of the red band. The NDVI value obtained through remote sensing images can be used to analyze the degree of vegetation coverage in the area. According to the different ranges of NDVI values, the threshold of vegetation coverage is set. Generally, a higher NDVI value indicates a higher vegetation coverage. By analyzing the vegetation coverage data in the soil erosion area, areas with high vegetation coverage can be identified. These areas usually have a stronger soil protection effect. In areas with high vegetation coverage, the roots of vegetation help to enhance the soil's resistance to erosion. By identifying these areas with high vegetation coverage, support is provided for subsequent ecological slope protection design.

[0203] Step S46: performing a regional intersection operation based on the surface uplift area data and the high vegetation coverage area data to obtain ecological slope protection data;

[0204] In the present embodiment, intersection operation is a spatial analysis method, which can extract the overlapping part of two areas. In this step, the surface uplift area and the high vegetation coverage area are subjected to intersection analysis, and the intersection area is the ecological slope protection area. These areas usually have relatively stable soil and stronger plant coverage, which are suitable for the construction of ecological slope protection. Intersection operation can be carried out by GIS software (such as ArcGIS or QGIS). In these software, the surface uplift area and the high vegetation coverage area can be selected to carry out intersection operation by means of superimposed layers, and final ecological slope protection area data are obtained. These data help to determine which areas are most suitable for the engineering design of ecological slope protection.

[0205] Step S47: performing ecological slope protection stability analysis based on the ecological slope protection uplift data and the soil landslide data to obtain ecological slope protection stability data.

[0206] In the present embodiment, it is necessary to analyze the ecological slope protection area data obtained by step S46 in combination with soil landslide data. Soil landslide data can be obtained through historical landslide events, soil structure, moisture data, etc. Through a stability analysis model (such as a landslide stability assessment model), factors such as the soil's anti-slip ability, vegetation coverage, and terrain slope are taken into consideration for a comprehensive assessment. In this process, common landslide stability calculation methods such as the limit equilibrium method and the finite element method can be used. By inputting the soil landslide data and combining the specific conditions of the ecological slope protection area, the stability of these areas is assessed to obtain ecological slope protection stability data. These data can provide the necessary basis for the ecological slope protection design of highways and ensure the long-term effectiveness and stability of slope protection measures.

[0207] Preferably, step S47 is specifically as follows:

[0208] Step S471: performing landslide impact force analysis based on soil landslide data to obtain landslide impact force data;

[0209] In this embodiment, the impact force of the landslide is generated by the combined effects of factors such as the mass, slope, and speed of the landslide body. In this step, soil landslide data is first obtained. These data generally include information such as the volume of the landslide body, the speed of the landslide, and the initial slope of the landslide. The calculation formula for the impact force of the landslide is generally F=m*a, where F is the impact force, m is the mass of the landslide body, and a is the acceleration. The mass of the landslide body can be obtained by multiplying the volume of the landslide by the soil density. The landslide speed can be measured or estimated through a model. The calculation of the landslide acceleration can be estimated based on the initial velocity, final velocity, and time of occurrence of the landslide. Based on these parameters, the impact force data corresponding to each landslide event can be calculated. For complex terrains, finite element analysis is also required to simulate the distribution of the impact force to ensure that more accurate impact force data is obtained.

[0210] Step S472: Calculating the sliding path length based on the landslide impact force data to obtain soil landslide length data;

[0211] In the present embodiment, it is necessary to determine the starting point and end point of the landslide path. The landslide path is usually composed of the source area of ​​the landslide body to the terminal area after the landslide. By analyzing the motion trajectory of the landslide body, in conjunction with the impact force data, the length of the landslide path can be calculated by terrain data (such as DEM, digital elevation model). Specifically, the calculation of the landslide path length can be realized by carrying out watershed analysis to DEM data. First, by determining the starting point position of the landslide body, use GIS software to carry out flow direction analysis, judge the path that the landslide body slides downward along the natural slope of the terrain. Then, obtain the length of the landslide path by calculating the total distance of the landslide body along this path. In path calculation, the influence of the gradient change and the acceleration of the landslide body on the path needs to be considered to ensure that accurate landslide path length data are obtained.

[0212] Step S473: Identify the impact area of ​​the ecological slope protection data based on the soil landslide length data to obtain the landslide impact data of the ecological slope protection data;

[0213] In this embodiment, according to the soil landslide length data obtained in the previous step, combined with the ecological slope protection area data, spatial overlay analysis is performed through GIS. In this process, the landslide path data and the ecological slope protection area data are used to perform an intersection operation to identify the overlapping part of the landslide path and the ecological slope protection area. Specifically, by setting an influence radius, usually the width of the landslide path, the area within a certain distance range on both sides of the landslide path will be regarded as the impact area of ​​the landslide. The setting of the influence radius can be adjusted according to the impact intensity of the landslide, the soil type and the vegetation coverage. The common influence radius can be 20-50 meters. When the landslide path overlaps with the ecological slope protection area, this part of the area is the area affected by the landslide, and the landslide impact data finally obtained will include the ecological slope protection area data covered by the landslide range.

[0214] Step S474: Evaluate the stability of the ecological slope protection based on the ecological slope protection data and the landslide impact data to obtain ecological slope protection stability data.

[0215] In this embodiment, based on the landslide impact data obtained in step S473, it is determined which ecological slope protection areas are affected by the landslide. In order to evaluate the stability of the ecological slope protection, a variety of factors can be combined, including the compaction of the soil, the root density of the vegetation, the terrain slope, etc. In actual operation, a stability assessment model is established by analyzing the terrain slope, soil type and vegetation coverage of the landslide-affected area. The stability assessment model can adopt methods such as the limit equilibrium method and the Mohr-Coulomb method to evaluate the stability of the soil under the impact of the landslide. For example, based on the friction angle, cohesion and slope of the soil, the critical conditions for the occurrence of the landslide are calculated. For areas affected by the landslide, the safety factor can be calculated based on the stability of the soil, and the stability data of the area can be obtained. Through this process, it can be determined which ecological slope protection areas still have good stability and which areas need further strengthening of protection measures.

[0216] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0217] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the stability of ecological slope protection on a highway, characterized in that: The following steps are involved: Step S1: Acquire remote sensing data of the highway monitoring area; perform soil erosion area detection based on the remote sensing data of the highway monitoring area to obtain soil erosion area data; Step S2: performing erosion intensity analysis based on the soil erosion area data to obtain erosion intensity data; extracting vegetation coverage data from the soil erosion area data; performing drainage blockage analysis based on the vegetation coverage data and erosion intensity data to obtain drainage blockage data; Step S3: performing soil stability analysis based on the drainage blockage data to obtain soil stability data; detecting soil compaction based on the soil stability data; performing soil landslide simulation based on the soil compaction to obtain soil landslide data; Step S4: Identify ecological slope protection areas based on the soil and water loss area data to obtain ecological slope protection data; perform ecological slope protection stability analysis based on the ecological slope protection data and soil landslide data to obtain ecological slope protection stability data.

2. The method for monitoring the stability of highway ecological slope protection according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Acquire remote sensing data of the highway monitoring area; Step S12: extracting vegetation index from remote sensing data of the highway monitoring area, and calculating vegetation coverage based on the vegetation index; Step S13: identifying bare soil areas according to vegetation coverage; Step S14: measuring the slope of the exposed soil area to obtain slope data; and calculating the high slope based on the slope data to obtain high slope data; Step S15: performing soil degradation detection based on the exposed soil area to obtain soil degradation data; Step S16: Identify the soil erosion area based on the high slope data and the soil degradation data to obtain soil erosion area data.

3. The method for monitoring the stability of highway ecological slope protection according to claim 2, characterized in that: Step S15 is specifically as follows: Step S151: collecting remote sensing images of the bare soil area based on the bare soil area; Step S152: dividing the band types according to the remote sensing image of the exposed soil area to obtain shortwave infrared band data and near infrared band data; Step S153: calculating the salinization index based on the short-wave infrared band data and the near-infrared band data; Step S154: Calculating shortwave infrared band reflectivity based on shortwave infrared band data; Step S155: performing soil moisture inversion based on the shortwave infrared band reflectivity to obtain soil moisture data; Step S156: soil degradation is determined based on the salinization index and soil moisture data to obtain soil degradation data.

4. The method for monitoring the stability of highway ecological slope protection according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: performing rainwater erosion simulation based on the soil erosion area data to obtain rainwater erosion data; Step S22: collecting soil samples based on rainwater erosion data, and determining the looseness of the soil structure of the soil samples; Step S23: estimating the anchoring strength of vegetation roots based on the looseness of the soil structure; Step S24: determining the degree of vegetation root breakage based on the anchoring strength of the vegetation root system; Step S25: determining the erosion intensity according to the degree of vegetation root breakage and the looseness of the soil structure to obtain erosion intensity data; Step S26: extracting vegetation coverage data from the soil erosion area data; Step S27: Perform drainage blockage analysis based on the vegetation coverage data and the erosion intensity data to obtain drainage blockage data.

5. The stability monitoring method for highway ecological slope protection according to claim 4 is characterized in that: Step S27 is specifically as follows: Step S271: drawing a vegetation coverage area map based on the vegetation coverage data; Step S272: extracting low vegetation coverage location information based on the vegetation coverage area map; Step S273: performing leaf density detection based on the low coverage position information to obtain leaf density data; Extract leaf sparse data from leaf density data; Step S274: performing a maximum rainfall simulation based on the low coverage location information to obtain maximum rainfall data; determining the water absorption capacity of the vegetation root system based on the maximum rainfall data to obtain the water absorption capacity data of the vegetation root system; Perform vegetation vitality analysis based on the water absorption capacity data of vegetation roots to obtain vegetation vitality data; extract vegetation low vitality data from the vegetation vitality data; Step S275: determining the soil exposure area based on the leaf sparseness data and the vegetation low vitality data; Step S276: Perform drainage blockage analysis based on the exposed soil area to obtain drainage blockage data.

6. The method for monitoring the stability of highway ecological slope protection according to claim 5, characterized in that: Step S276 is specifically as follows: Soil wind erosion simulation is performed based on the soil exposure area to obtain soil wind erosion data; Detect soil moisture based on soil wind erosion data to obtain soil moisture data; Wet soil is identified based on soil moisture data to obtain wet soil data; Identify coarse-grained soil based on soil wind erosion data to obtain coarse-grained soil data; Perform soil transport simulation based on moist soil data and coarse-grained soil data to obtain soil transport data; Obtain highway drainage paths; identify curved drainage paths; Drainage blockage analysis is performed based on the curved drainage path and soil transport data to obtain drainage blockage data.

7. The method for monitoring the stability of highway ecological slope protection according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: determining the drainage retention area based on the drainage blockage data; Step S32: performing soil moisture evaporation analysis on the drainage retention area to obtain soil moisture evaporation data; Step S33: Divide the soil layers based on the soil moisture evaporation data to obtain upper soil layer data and lower soil layer data; Step S34: calculating the upper soil moisture according to the upper soil data to obtain the upper soil moisture; calculating the lower soil moisture according to the lower soil data to obtain the lower soil moisture; and calculating the humidity gradient according to the upper soil moisture and the lower soil moisture to obtain the humidity gradient data. Step S35: determining soil moisture tension based on the moisture gradient data, simulating soil surface crack growth based on the soil moisture tension, and obtaining soil surface crack data; evaluating soil stability based on the soil surface crack data, and obtaining soil stability data; Step S36: detecting soil compaction according to soil stability data; Step S37: Perform soil landslide simulation based on soil compaction to obtain soil landslide data.

8. The method for monitoring the stability of ecological slope protection of highway according to claim 7, characterized in that: Step S36 is specifically as follows: Step S361: Analyzing the erosion risk based on soil stability data to obtain soil erosion risk; Step S362: predicting soil loss rate based on soil erosion risk; Step S363: determining the remaining soil amount according to the soil loss rate; Step S364: Evaluate the soil compaction degree based on the soil remaining amount.

9. The method for monitoring the stability of ecological slope protection of highway according to claim 8, characterized in that: Step S4 is specifically as follows: Step S41: marking the soil erosion boundary according to the soil erosion area data; Step S42: extracting terrain relief data based on the soil erosion area data; Step S43: Calculating the slope change rate based on the terrain relief data; Step S44: detecting the surface uplift area of ​​the soil erosion boundary according to the slope change rate to obtain surface uplift area data; Step S45: detecting vegetation coverage based on the soil and water loss area data; identifying high vegetation coverage areas based on the vegetation coverage, and obtaining high vegetation coverage area data; Step S46: performing a regional intersection operation based on the surface uplift area data and the high vegetation coverage area data to obtain ecological slope protection data; Step S47: performing ecological slope protection stability analysis based on the ecological slope protection uplift data and the soil landslide data to obtain ecological slope protection stability data.

10. The method for monitoring the stability of ecological slope protection of highway according to claim 8, characterized in that: Step S47 is specifically as follows: Step S471: performing landslide impact force analysis based on soil landslide data to obtain landslide impact force data; Step S472: Calculating the sliding path length based on the landslide impact force data to obtain soil landslide length data; Step S473: Identify the impact area of ​​the ecological slope protection data based on the soil landslide length data to obtain the landslide impact data of the ecological slope protection data; Step S474: Evaluate the stability of the ecological slope protection based on the ecological slope protection data and the landslide impact data to obtain ecological slope protection stability data.

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