Method for Screening and Configuring Management of Highway Ecological Greening Vegetation

Abstract

The present invention discloses a method for screening and configuring management of ecological greening vegetation on expressways, specifically relating to the technical field of vegetation management; by extracting the three-dimensional roughness characteristics of the ground surface based on the digital elevation model, combining with the reprojection error residuals in the image registration process, establishing a multi-source data registration consistency evaluation model, accurately identifying potential conflict areas where coordinate system errors are concentrated, and using a deformation control algorithm to perform local coordinate fine-tuning and elevation correction on them, significantly improving the positioning accuracy of the vegetation distribution boundary and the operation area, thereby realizing the refined and intelligent management of greening operations, avoiding incorrect pruning operations, and ensuring the stable ecological function.

Description

Technical Field

[0001] The present invention relates to the technical field of vegetation screening and configuration management, and specifically to a method for screening and configuring management of ecological greening vegetation on expressways. Background Art

[0002] The screening and configuration management of ecological greening vegetation on expressways refers to scientifically selecting suitable plant species according to local environmental conditions such as climate, soil, and terrain, and reasonably arranging their planting positions and densities when carrying out ecological greening along expressways, so as to achieve the purposes of beautifying the environment, preventing soil erosion, and improving the ecosystem. At the same time, through effective management measures, ensure the long-term stable growth of vegetation and improve the greening effect and ecological benefits.

[0003] The existing technologies have the following deficiencies:

[0004] When multi-source data (remote sensing, UAV, ground survey) is integrated into the GIS system, due to spatial coordinate system errors or elevation registration deviations, the vegetation management area is mismatched, affecting the greening maintenance operation. For example, when using the GIS system to plan the pruning route of trees and shrubs, the UAV images merged in the system may mislocate some shrub areas to the central isolation belt because different benchmarks are used in the coordinate system. When the maintenance team prunes according to the map, the shrubs with good ecological protection effects on one side are mispruned, resulting in local exposure, increasing the risk of wind erosion and being difficult to repair afterwards. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for screening and configuring management of ecological greening vegetation on expressways to solve the deficiencies in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: A method for screening and configuring management of ecological greening vegetation on expressways, including:

[0007] Collect multi-source data along the expressway, including remote sensing images, UAV images, ground RTK measurement data, and digital elevation models, and perform format conversion and preliminary coordinate unification;

[0008] Obtain the surface three-dimensional roughness characteristics based on the digital elevation model to identify complex terrain areas. Based on the identification results, perform georegistration on the remote sensing or UAV images to obtain the re-projection error residual characteristics within the complex terrain areas for evaluating the spatial registration accuracy;

[0009] Perform spatial overlay analysis on the surface three-dimensional roughness characteristics and re-projection error residual characteristics within the complex terrain areas, establish a multi-source data registration consistency evaluation model, and identify potential conflict areas with concentrated coordinate system errors;

[0010] Adjust the local coordinate system and elevation correction for potential conflict areas, use the deformation control algorithm for fine calibration, and import the corrected data into the GIS system to update the vegetation distribution and operation boundary, so as to achieve precise management of vegetation screening, configuration and maintenance operations.

[0011] Preferably, within each window, calculate the three-dimensional surface roughness value. Specifically: set a sliding window of size N×N for the input digital elevation model image; take the central pixel of the window as the analysis target, extract the elevation value and its corresponding spatial coordinates in its neighborhood; within each window, use the least squares method to fit a local surface, and the fitting model adopts the quadratic surface form. For each point in the window, calculate the difference between its actual elevation and the elevation of the fitting surface, and calculate the root mean square value of the elevation residuals of all points in the window as the three-dimensional surface roughness value of the pixel point.

[0012] Preferably, assign the three-dimensional surface roughness value calculated for each sliding window to the central pixel of the window to form a new three-dimensional surface roughness index layer, and set a roughness threshold; identify the area exceeding the threshold as a terrain complex area.

[0013] Preferably, after registration, calculate the reprojection error residuals of each ground control point. The calculation method is: express the relationship between the ground coordinates (X, Y) and the image coordinates (x, y) as: ; where 、 are transformation parameters obtained by fitting the control points using the least squares method. According to the error propagation theory solved by the least squares method, the covariance matrix of the transformation parameters is calculated as: ; where: A is the design matrix; is the unit weight error, is the covariance main term of the normal equation;

[0014] Propagate the error of the control point coordinates to the image point , and calculate the covariance matrix of the reprojection error, and the expression is: ; where: is the partial derivative Jacobian matrix at , and T is the matrix transpose;

[0015] Extract the main direction error , the secondary direction error and the direction angle of the reprojection error covariance matrix respectively, and perform weighted summation calculation on the main direction error and the secondary direction error under all direction angles to obtain the reprojection error residuals.

[0016] Preferably, the three-dimensional surface roughness value and the reprojection error residual are subjected to dimensionless normalization processing so that they are both within [0, 1], and the multi-source registration error conflict score value of each region is calculated based on the normalized three-dimensional surface roughness value and the reprojection error residual.

[0017] Preferably, the scores of each ground control point are spatially interpolated into a continuous heat map layer. Using the inverse distance weighting method, a registration conflict risk heat map is obtained, and a conflict score threshold is set; the continuous regions where the multi-source registration error conflict score value exceeds the conflict score threshold are extracted as potential conflict regions and output as a vector surface layer.

[0018] Preferably, using the output conflict region vector surface layer, the region boundary is extracted, auxiliary ground control points are densely arranged within the region, and a deformation control algorithm is used for coordinate system adjustment. Specifically: Let the point in the original image be , and the true coordinate be ; by minimizing the energy function, the smooth transformation function is fitted to make the point pairs coincide before and after deformation;

[0019] Within the same region, DEM elevation data is extracted, the original elevation is compared with the measured elevation of the auxiliary control points, and the elevation residual is calculated; the spline interpolation method is used to generate an elevation residual correction grid within the region; the orthophoto image and the vector boundary map obtained after elevation interpolation correction are exported in the format of a unified projection coordinate system;

[0020] The corrected data is imported into the GIS platform to replace the old layer; the vegetation distribution map, the operation boundary map, and the risk warning map are updated, and the updated layers are used for vegetation screening and management.

[0021] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0022] 1. By introducing three-dimensional surface roughness analysis and reprojection error residual evaluation, the present invention establishes a multi-source data registration consistency evaluation model, which can accurately identify potential conflict regions. On this basis, combined with the deformation control algorithm and the elevation correction interpolation technology, the refined calibration of the local coordinate system and elevation data is realized, and the problem of spatial positioning deviation is solved from the source, significantly improving the accuracy and reliability of the greening operation data.

[0023] 2. By uniformly importing the corrected multi-source data into the GIS system, the present invention can automatically update the vegetation distribution map, operation boundary map, and risk warning map, achieving high-precision management of ecological greening operations such as pruning of trees and shrubs and vegetation replanting. The overall technical path integrates means such as terrain perception, error propagation modeling, and spatial intelligent analysis, with strong adaptability and popularization value, effectively avoiding ecological damage and repeated operations caused by data errors, and improving the intelligent, scientific, and sustainable management level of highway ecological greening. Brief Description of the Drawings

[0024] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0025] Figure 1 It is the method flowchart of the present invention. Detailed Embodiments

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0027] Embodiment, please refer to Figure 1 As shown, the method for screening and configuring highway ecological greening vegetation in this embodiment includes:

[0028] Collect multi-source data along the highway, including remote sensing images, UAV images, ground RTK measurement data, and digital elevation models, and perform format conversion and preliminary coordinate unification;

[0029] Based on the digital elevation model, obtain the surface three-dimensional roughness characteristics for identifying complex terrain areas. Based on the identification results, perform georegistration on the remote sensing or UAV images to obtain the reprojection error residual characteristics within the complex terrain areas for evaluating the spatial registration accuracy;

[0030] Perform spatial overlay analysis on the surface three-dimensional roughness characteristics and reprojection error residual characteristics within the complex terrain areas, establish a multi-source data registration consistency evaluation model, and identify potential conflict areas where coordinate system errors are concentrated;

[0031] Fine-tune the local coordinate system and correct the elevation of potential conflict areas, use deformation control algorithms for fine-tuning calibration, and import the corrected data into the GIS system to update vegetation distribution and work boundaries, thereby achieving precise management of vegetation screening, configuration and maintenance operations.

[0032] Collect remote sensing images: Obtain multi-temporal remote sensing images of the target highway area from high-resolution satellite remote sensing platforms (such as Gaofen series, Sentinel-2, Landsat-8, etc.). Acquisition parameter requirements: The spatial resolution is preferably less than 1 meter, the acquisition time should be cloudless and during the vegetation growth period, and support multi-spectral bands (including red light, near infrared, etc.). Application purpose: Used for macro vegetation cover analysis, growth monitoring, disease identification and GIS base map establishment.

[0033] Collect drone images: Use drones equipped with high-precision GPS and multispectral / visible light cameras to conduct low-altitude aerial photography over the target highway section, with the flight altitude generally controlled between 50 and 120 meters. Image processing requirements: Use image processing software (such as Pix4D, DroneDeploy) to perform orthorectification, image mosaic, and aerial triangulation on the original image to generate an orthophoto map (DOM) and a preliminary three-dimensional model (DSM). Application purpose: Used for high-precision green area identification, shrub / tree boundary extraction, detailed inspection of local work areas, etc.

[0034] Obtaining ground RTK measurement data: Deploy GNSS-RTK measurement points at typical locations along the highway (such as central isolation belts, slope toe, plant planting points, etc.), and record high-precision three-dimensional coordinates (within ±2cm in plane and ±5cm in elevation). Measurement point design: Select representative ground features with strong coverage (such as guardrail bases, corners of structures, typical vegetation areas), and record point attributes and ground feature types. Application purpose: Provide ground control points (GCPs) for the registration of drone images and remote sensing images, and also for DEM verification and elevation correction.

[0035] Obtain digital elevation model (DEM): Public DEM (such as SRTM, ASTER GDEM) can be selected for preliminary modeling. In key areas, drones need to be used to generate DSM and combined with RTK data for terrain restoration to generate high-precision DEM. Resolution requirements: The resolution is preferably 5-10 meters or higher to ensure the accuracy of micro-topography such as slopes and green belts. Application purpose: Used for slope, aspect analysis and elevation registration, which is of great reference significance for vegetation type selection and planting design.

[0036] Unify the conversion of remote sensing images and UAV images into GeoTIFF or JPEG2000 format, save the DEM as GeoTIFF format, and unify the vector data into Shapefile (.shp) or GeoJSON format. Project all data onto a unified geographic coordinate system (such as WGS 84) or projected coordinate system (such as CGCS2000 / UTM Zone) to ensure spatial alignment. Tool platform: Use tools such as ArcGIS, QGIS, ENVI, or GDAL for conversion and reprojection operations.

[0037] Image registration with RTK points: Georeference the UAV images using GCPs, calculate the reprojection error, and remove the control points with large errors. Alignment of multi-source layers: Overlay the remote sensing images, UAV images, DEM, and vector maps in the GIS platform, and check whether the edges and linear features (such as roads and rivers) match. Conduct error analysis on the coordinate accuracy of all layers, record the error residual (RMS) value, and output for subsequent processing after ensuring the accuracy is within the preset range.

[0038] Analyze the elevation change degree of the ground surface at the microscale through the digital elevation model (DEM), calculate the three-dimensional roughness characteristic values, so as to identify the areas with severe terrain undulation and complex geomorphic structures, providing a basis for subsequent elevation registration optimization, slope stability analysis, and vegetation type configuration.

[0039] High-resolution digital elevation model (DEM), preferably with a resolution of 1 - 10 meters; Optional auxiliary data: slope map, aspect map, geomorphic zoning map, etc.

[0040] Set a sliding window of a fixed size (such as 3×3, 5×5, or 7×7 pixels) on the DEM for local elevation statistical analysis. The size of the sliding window needs to be reasonably selected according to the DEM resolution and the scale of the target area.

[0041] Within each window, calculate the three-dimensional roughness value of the ground surface. Specifically: Set a sliding window of size N×N (such as 3×3, 5×5, 7×7) for the input digital elevation model (DEM) image; Take the central pixel of the window as the analysis target, and extract the elevation values within its neighborhood , where represents the actual elevation value (from the DEM) of the i-th grid point and its corresponding spatial coordinates .

[0042] Within each window, fit a local surface using the least squares method. The fitting model usually adopts the quadratic surface form: ; The fitting parameters a, b, c, d, e, f are obtained by minimizing the sum of squared errors: ; For each point within the window, calculate the difference between the actual elevation of the point and the elevation of the fitted surface , and the expression is: Calculate the root mean square (RMS) value of the elevation residuals for all points within the window as the three-dimensional surface roughness value of the pixel point. .

[0043] Assign the three-dimensional surface roughness value calculated for each sliding window to the pixel at the window center to form a new three-dimensional surface roughness index layer; the larger the three-dimensional surface roughness value, the more obvious the surface undulation and the higher the roughness in the area.

[0044] Set the roughness threshold through statistical analysis or empirical judgment; identify the areas exceeding the threshold as complex terrain areas and output them as vector polygon layers (such as.shp format).

[0045] It should be noted here that areas with high roughness are more likely to generate DEM registration errors and can be used as areas for enhancing the layout of local control points; avoid using ground cover plants that require large-area leveling in complex terrain areas and give priority to configuring shrubs with strong root soil-fixing ability or local drought-tolerant plants; areas with high roughness often correspond to potential landslide or erosion-sensitive areas and can be used for early warning and reinforcement design.

[0046] In complex terrain areas, after georeferencing remote sensing or UAV images, judge the accuracy and stability of spatial registration by analyzing the characteristics of reprojection error residuals, and identify possible deformation areas or error concentration areas, so as to ensure the spatial consistency of data fusion, specifically including:

[0047] Select obvious, easily recognizable, and stable ground objects in remote sensing or UAV images as ground control points (such as road intersections, guardrail posts, bridge corners); at the same time, obtain the high-precision three-dimensional coordinates of these points in the field (provided by RTK measurement or known layers); the number and distribution of control points need to evenly cover the entire image area, and the density in complex terrain areas should be appropriately increased.

[0048] Use image processing software (such as Pix4D, Agisoft Metashape, ArcGIS) to perform georeferencing; select an appropriate transformation model (such as affine transformation, polynomial transformation, TPS, etc.) to convert the image coordinate system to a unified projection coordinate system (such as WGS 84 / UTM); the registration result should be optimized using the method of minimum error.

[0049] After registration, the system automatically calculates the reprojection error residuals of each ground control point. The calculation method is: express the relationship between the ground coordinates (X, Y) and the image coordinates (x, y) as:

[0050] ; where , is a transformation parameter obtained by fitting control points with the least squares method. According to the error propagation theory solved by the least squares method, the covariance matrix of the transformation parameter The calculation expression is: ; where: A is the design matrix (including control point coordinates); is the error of unit weight (which can be estimated as the mean of the sum of squared residuals), is the main term of the covariance of the normal equation.

[0051] Propagate the error of the control point coordinates to the image point , and calculate the covariance matrix of the reprojection error. The expression is: ; where: is the partial derivative Jacobian matrix at , and T is the matrix transpose.

[0052] Calculate the error ellipse parameters and residual quantization index. Extract the main characteristic indexes of the reprojection error residuals from : the main direction error (major axis): ; the minor direction error (minor axis): ; the direction angle (error ellipse direction): can be obtained from the eigenvector. The main direction error and minor direction error at all direction angles are weighted and summed to calculate the reprojection error residuals.

[0053] Set a reasonable error tolerance (such as <1.0m); if the proportion of points with reprojection error residuals greater than the error tolerance in the complex terrain area exceeds the threshold (such as >20%), it is determined that the registration accuracy of this area is insufficient, and the control points need to be recalibrated or the distribution optimized.

[0054] Perform dimensionless normalization on the surface three-dimensional roughness value and the reprojection error residuals so that they are both in the range of [0,1]. Calculate the multi-source registration error conflict score value of each area according to the normalized surface three-dimensional roughness value and reprojection error residuals.

[0055] For example, the present invention can calculate the multi-source registration error conflict score value by using the following multi-source data registration consistency evaluation model formula. The calculation expression is: ; in the formula, is the multi-source registration error conflict score value, is the normalized surface three-dimensional roughness value, is the normalized reprojection error residual, is the weight coefficient of the normalized surface three-dimensional roughness value and reprojection error residual (which can be optimized according to experimental experience or machine learning), and are all greater than 0.

[0056] Score each ground control point Spatially interpolate the scores of each ground control point into a continuous heatmap layer using the inverse distance weighted method to obtain a heatmap of registration conflict risk, and set a conflict score threshold (e.g., 0.7); extract the continuous area where the multi-source registration error conflict score value exceeds the conflict score threshold as the potential conflict area and output it as a vector polygon layer.

[0057] For the identified registration conflict areas, perform refined corrections of local coordinates and elevations to eliminate spatial positioning deviations caused by data source differences or complex terrain, and ensure the accuracy of the subsequent greening vegetation operation boundaries and configuration areas.

[0058] Using the output vector polygon layer of the conflict area, extract the area boundary; densely deploy auxiliary GCPs (ground control points) or feature matching points within the area, which can be achieved by: on-site RTK measurement; automatic image feature matching (such as SIFT, ORB); comparison assisted by existing layers.

[0059] Perform fine-tuning of the coordinate system using a deformation control algorithm. Select the algorithm: Thin Plate Spline (TPS) surface transformation. Specifically: Let the points in the original image be , and the target (true coordinates) be ; by minimizing the energy function, fit the smooth transformation function to make the point pairs before and after deformation coincide as much as possible: ; where: is the radial basis function, is the control coefficient that controls the deformation intensity, m is the total number of points in the original image, controls the overall translation and rotation.

[0060] Within the same area, extract DEM elevation data; compare the original elevation with the measured elevation z of the auxiliary control points, calculate the elevation residuals; use spline interpolation to generate an elevation error correction grid within the area; export the orthophoto image (DOM), DEM, and vector boundary maps (vegetation boundaries, operation zoning) obtained after elevation interpolation correction into a unified projection coordinate system (such as CGCS2000 / UTM) format; output formats: GeoTIFF (raster), Shapefile (vector), GeoJSON (web service), etc.

[0061] Import the corrected data into a GIS platform (such as ArcGIS / QGIS) and replace the old layers; update the following layer content:

[0062] Vegetation distribution map (such as the distribution of shrubs, trees, lawn areas); operation boundary map (such as pruning areas, replanting areas, sprinkler belts, etc.); risk warning map (such as misregistration areas, high error area markings).

[0063] Using the updated layer and combining with the attribute database, the following functions are realized:

[0064] Vegetation screening suggestions: Output the optimal vegetation types by combining terrain and spatial accuracy; Greening configuration optimization: Output the vegetation combination and density layout plan; Maintenance operation accuracy control: Combine GPS navigation to control the operation path and the operation boundary of the equipment; Error traceability and correction record management: Archive the calibration data each time for easy traceability and analysis.

[0065] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should be covered within the protection scope of the present application.

Claims

1. Method for screening and configuring management of ecological greening vegetation on expressways, characterized in that: Including: Collect multi-source data along the highway, including remote sensing images, UAV images, ground RTK measurement data and digital elevation models, and perform format conversion and preliminary coordinate unification; Obtain the surface three-dimensional roughness characteristics based on the digital elevation model to identify terrain complex areas. Based on the identification results, perform georegistration on remote sensing or UAV images to obtain the reprojection error residual characteristics within the terrain complex areas for evaluating the spatial registration accuracy; Conduct a spatial overlay analysis on the surface three-dimensional roughness characteristics and reprojection error residual characteristics within the terrain complex areas, establish a multi-source data registration consistency evaluation model, and identify potential conflict areas where coordinate system errors are concentrated; Specifically including: performing dimensionless normalization processing on the surface three-dimensional roughness values and reprojection error residuals so that they are both within [0,1], and calculating the multi-source registration error conflict score values for each area according to the normalized surface three-dimensional roughness values and reprojection error residuals; Score each ground control point Spatially interpolate the scores of each ground control point into a continuous heat map layer using the inverse distance weighted method to obtain a heat map of registration conflict risk, and set a conflict score threshold; extract the continuous areas where the multi-source registration error conflict score values exceed the conflict score threshold as potential conflict areas and output them as vector surface layers; Adjust the local coordinate system and correct the elevation for the potential conflict areas, perform refined calibration using the deformation control algorithm, and import the corrected data into the GIS system to update the vegetation distribution and operation boundaries, realizing precise management of vegetation screening, configuration and maintenance operations; Specifically, it includes: using the output conflict area vector surface layer to extract the area boundary, densely arranging auxiliary ground control points within the area, and adopting a deformation control algorithm for coordinate system adjustment. Specifically: select the TPS surface transformation, and set the points in the original image as , and the true coordinates are ; by minimizing the energy function, fitting the smooth transformation function f(x,y) to make the point pairs coincide before and after deformation; Within the same area, extract DEM elevation data, compare the original elevation with the measured elevation of the auxiliary ground control points, and calculate the elevation residuals; use the spline interpolation method to generate an elevation residual correction raster within the area; export the orthoimage and vector boundary map obtained after elevation interpolation correction into the unified projection coordinate system format; Import the corrected data into the GIS platform and replace the old layers; update the vegetation distribution map, operation boundary map and risk warning map, and use the updated layers for vegetation screening and management.

2. The method for screening and configuring management of highway ecological greening vegetation according to claim 1, characterized in that: Calculate the surface three-dimensional roughness value within each window, specifically: set a sliding window of size N×N for the input digital elevation model image; take the pixel at the center of the window as the analysis target, extract the elevation values and their corresponding spatial coordinates in its neighborhood; within each window, fit a local surface using the least squares method, and the fitting model adopts the quadratic surface form. For each point within the window, calculate the difference between its actual elevation and the elevation of the fitting surface, and calculate the root mean square value of the elevation residuals of all points within the window as the surface three-dimensional roughness value of the pixel point.

3. The method for screening and configuring management of highway ecological greening vegetation according to claim 2, characterized in that: Assign the surface three-dimensional roughness value calculated for each sliding window to the pixel at the center of the window to form a new surface three-dimensional roughness index layer, and set a roughness threshold; identify the areas exceeding the threshold as terrain complex areas.

4. The method for screening and configuring management of highway ecological greening vegetation according to claim 3, wherein: After registration is completed, calculate the reprojection error residuals of each ground control point. The calculation method is as follows: Express the relationship between the ground coordinates (X, Y) and the image coordinates (x, y) as: ; where are transformation parameters obtained by fitting the control points using the least squares method. According to the error propagation theory solved by the least squares method, the covariance matrix of the transformation parameters has the following calculation expression: ; where: A is the design matrix; is the unit weight error, is the covariance main term of the normal equation; Propagate the error of the control point coordinates to the image points , calculate the covariance matrix of the reprojection error , and the expression is: ; where: is the partial derivative Jacobian matrix at , and T is the matrix transpose; Extract the main direction error, secondary direction error , and direction angle of the reprojection error residuals respectively according to the covariance matrix of the reprojection error. After weighted summation calculation of the main direction error and secondary direction error at all direction angles, the reprojection error residuals are obtained. ​

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