Expressway ecological greening vegetation screening and configuration management method

Abstract

The invention discloses an expressway ecological greening vegetation screening and configuration management method, and particularly relates to the technical field of vegetation management. A multi-source data registration consistency evaluation model is established by extracting earth surface three-dimensional roughness features based on a digital elevation model and combining with a reprojection error residual in an image registration process, a potential conflict area with concentrated coordinate system errors is accurately identified, local coordinate fine adjustment and elevation correction are performed on the potential conflict area by adopting a deformation control algorithm, and a multi-source data registration consistency evaluation model is established. The positioning precision of the vegetation distribution boundary and the operation area is remarkably improved, so that refined and intelligent management of greening operation is realized, wrong shearing and wrong cropping are avoided, and the stability of the ecological function is guaranteed.

Description

Technical Field

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

[0002] The screening and configuration management of ecological greening vegetation for highways refers to the scientific selection of suitable plant species and the reasonable matching of their planting positions and densities according to local climate, soil, topography and other environmental conditions when carrying out ecological greening along highways, so as to achieve the purpose of beautifying the environment, preventing soil erosion and improving the ecosystem. At the same time, effective management measures are taken to ensure the long-term stable growth of vegetation and improve the greening effect and ecological benefits.

[0003] The prior art has the following deficiencies: When multi-source data (remote sensing, drones, ground surveys) are integrated into the GIS system, errors in the spatial coordinate system or deviations in elevation registration may cause mismatches in vegetation management areas, affecting greening maintenance operations. For example, when using the GIS system to plan tree and shrub pruning routes, the drone images merged into the system may cause some shrub areas to be mislocated to the central isolation belt because the coordinate system uses different benchmarks. When the maintenance team was pruning according to the map, they mistakenly pruned shrubs with good ecological protection on one side, causing partial exposure, increasing the risk of wind erosion, and making it difficult to repair afterwards. Summary of the invention

[0004] The purpose of the present invention is to provide a method for screening and configuring highway ecological greening vegetation to solve the shortcomings of the background technology.

[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for screening and configuring highway ecological greening vegetation, comprising: Collect multi-source data along the highway, including remote sensing images, drone images, ground RTK measurement data and digital elevation models, and perform format conversion and preliminary coordinate unification; The three-dimensional roughness characteristics of the surface are obtained based on the digital elevation model to identify areas with complex terrain. Based on the identification results, the remote sensing or UAV images are geo-referenced to obtain the residual characteristics of the reprojection error in the complex terrain area to evaluate the accuracy of spatial registration. Perform spatial overlay analysis on the three-dimensional surface roughness characteristics and reprojection error residual characteristics in complex terrain areas, establish a multi-source data registration consistency evaluation model, and identify potential conflict areas where coordinate system errors are concentrated; The local coordinate system and elevation correction are carried out in the potential conflict area, and the deformation control algorithm is used for fine calibration. The corrected data is imported into the GIS system to update the vegetation distribution and operation boundaries, so as to realize the precise management of vegetation screening, configuration and maintenance operations.

[0006] Preferably, the three-dimensional roughness value of the surface is calculated in each window, specifically: a sliding window of size N×N is set for the input digital elevation model image; the central pixel of the window is taken as the analysis target, and the elevation value and its corresponding spatial coordinates are extracted in its neighborhood; in each window, a local surface is fitted using the least squares method, and the fitting model takes the form of a quadratic surface. For each point in the window, the difference between its actual elevation and the elevation of the fitted surface is calculated, and the root mean square value of the elevation residuals of all points in the window is calculated as the three-dimensional roughness value of the surface of the pixel point.

[0007] Preferably, the three-dimensional surface roughness value calculated by each sliding window is assigned to the central pixel of the window to form a new three-dimensional surface roughness index layer, and a roughness threshold is set; and the area exceeding the threshold is identified as a complex terrain area.

[0008] Preferably, after the registration is completed, the reprojection error residual of each ground control point is calculated by: the relationship between the ground coordinates (X, Y) and the image coordinates (x, y) is expressed as: ;in , is the transformation parameter, which is 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 parameter is The calculation expression is: ; Where: A is the design matrix; is the unit weight error, is the principal term of the covariance of the normal equation; The control point coordinates The error is propagated to the image point , calculate the covariance matrix of the reprojection error , the expression is: ;in: is The Jacobian matrix of partial derivatives at , T is the matrix transpose; According to the covariance matrix of the reprojection error, the main direction error of the reprojection error residual is extracted , secondary direction error And the direction angle, the weighted sum of the main direction error and the secondary direction error under all direction angles is calculated to get the reprojection error residual.

[0009] Preferably, the three-dimensional surface roughness value and the reprojection error residual are dimensionless and normalized so that they are both between [0,1], and the multi-source registration error conflict score of each region is calculated based on the normalized three-dimensional surface roughness value and the reprojection error residual.

[0010] Preferably, the score of each ground control point is Spatial interpolation is a continuous heat map layer. The inverse distance weighted method is used to obtain a registration conflict risk heat map and set a conflict score threshold. The continuous areas where the multi-source registration error conflict score value exceeds the conflict score threshold are extracted as potential conflict areas and output as a vector surface layer.

[0011] Preferably, the output conflict area vector surface layer is used to extract the area boundary, and auxiliary ground control points are densely arranged in the area. The coordinate system is adjusted using the deformation control algorithm. Specifically, the points in the original image are set as , the real coordinates are ; Fitting a smooth transformation function by minimizing the energy function , so that the point pairs before and after deformation coincide; In the same area, extract DEM elevation data, compare the original elevation with the measured elevation of the auxiliary control points, and calculate the elevation residual; use the spline interpolation method to generate an elevation residual correction grid in the area; export the orthophoto and vector boundary map obtained after elevation interpolation correction into a unified projection coordinate system format; Import the corrected data into the GIS platform to replace the old layers; update the vegetation distribution map, work boundary map and risk warning map, and use the updated layers to perform vegetation screening and management.

[0012] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. This invention introduces three-dimensional surface roughness analysis and reprojection error residual evaluation to establish a multi-source data registration consistency evaluation model, which can accurately identify potential conflict areas. On this basis, combined with deformation control algorithm and elevation correction interpolation technology, the local coordinate system and elevation data are finely calibrated, which solves the problem of spatial positioning deviation from the source and significantly improves the accuracy and reliability of greening operation data.

[0013] 2. The present invention can automatically update vegetation distribution maps, operation boundary maps and risk warning maps by uniformly importing the corrected multi-source data into the GIS system, thus realizing high-precision management of ecological greening operations such as tree and shrub pruning and vegetation replanting. The overall technical path integrates terrain perception, error propagation modeling and spatial intelligent analysis, has strong adaptability and promotion value, effectively avoids ecological damage and repeated operations caused by data errors, and improves the intelligent, scientific and sustainable management level of highway ecological greening. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0015] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] For examples, see Figure 1 As shown, the highway ecological greening vegetation screening and configuration management method described in this embodiment includes: Collect multi-source data along the highway, including remote sensing images, drone images, ground RTK measurement data and digital elevation models, and perform format conversion and preliminary coordinate unification; The three-dimensional roughness characteristics of the surface are obtained based on the digital elevation model to identify areas with complex terrain. Based on the identification results, the remote sensing or UAV images are geo-referenced to obtain the residual characteristics of the reprojection error in the complex terrain area to evaluate the accuracy of spatial registration. Perform spatial overlay analysis on the three-dimensional surface roughness characteristics and reprojection error residual characteristics in complex terrain areas, establish a multi-source data registration consistency evaluation model, and identify potential conflict areas where coordinate system errors are concentrated; 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] Convert remote sensing images and drone images to GeoTIFF or JPEG2000 format, save DEM in GeoTIFF format, and convert vector data to Shapefile (.shp) or GeoJSON format. Project all data to a unified geographic coordinate system (such as WGS 84) or projection 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.

[0023] Image and RTK point registration: Use GCP to georeference drone images, calculate reprojection errors, and remove control points with large errors. Multi-source layer alignment: Overlay remote sensing images, drone images, DEMs, and vector maps in the GIS platform to check whether edges and linear features (such as roads and rivers) match. Perform error analysis on the coordinate accuracy of all layers, record the error residual (RMS) value, and ensure that the accuracy is within the preset range before outputting it for subsequent processing.

[0024] The digital elevation model (DEM) is used to analyze the elevation changes of the surface at a microscale and calculate the three-dimensional roughness characteristic values, so as to identify areas with dramatic terrain undulations and complex landform structures, providing a basis for subsequent elevation registration optimization, slope stability analysis and vegetation type configuration.

[0025] High-resolution digital elevation model (DEM), preferably with a resolution of 1–10 m; optional auxiliary data: slope map, aspect map, geomorphic zoning map, etc.

[0026] A fixed-size sliding window (such as 3×3, 5×5, or 7×7 pixels) is set on the DEM for local elevation statistical analysis. The sliding window size should be reasonably selected based on the DEM resolution and the scale of the target area.

[0027] In each window, the three-dimensional roughness value of the surface is calculated. Specifically, a sliding window of size N×N (such as 3×3, 5×5, 7×7) is set for the input digital elevation model (DEM) image; the central pixel of the window is used as the analysis target, and the elevation value is extracted in its neighborhood. , represents the actual elevation value of the i-th grid point (from DEM) and its corresponding spatial coordinates .

[0028] In each window, a local surface is fitted using the least squares method. The fitting model usually takes the form of a quadratic surface: ; The fitting parameters a, b, c, d, e, and f are obtained by minimizing the sum of squared errors: ; For each point in the window, calculate the difference between the actual elevation of the point and the elevation of the fitted surface , the expression is: ; Calculate the root mean square (RMS) value of the elevation residual of all points in the window as the three-dimensional surface roughness value of the pixel point .

[0029] The three-dimensional surface roughness value calculated by each sliding window is assigned to the central pixel of the window to form a new three-dimensional surface roughness index layer; the larger the three-dimensional surface roughness value, the more obvious the surface undulation in the area and the higher the roughness.

[0030] The roughness threshold is set through statistical analysis or empirical judgment; the area exceeding the threshold is identified as a complex terrain area, which can be output as a vector surface layer (such as .shp format).

[0031] It should be noted here that areas with high roughness are more likely to produce DEM registration errors and can be used as local control points to enhance the layout area; in complex terrain areas, avoid using ground cover plants that require large areas to be leveled, and give priority to shrubs with strong root soil-fixing capabilities or local drought-resistant plants; areas with high roughness often correspond to potential landslides or erosion-sensitive areas and can be used for early warning and reinforcement design.

[0032] In complex terrain areas, after georeferencing remote sensing or UAV images, the accuracy and stability of spatial registration are determined by analyzing the residual characteristics of reprojection errors, and possible deformation areas or error concentration areas are identified to ensure the spatial consistency of data fusion. Specifically, the following are included: Select obvious, identifiable and stable features 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 must evenly cover the entire image area, and the density should be appropriately increased in areas with complex terrain.

[0033] 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 error minimization method should be used to optimize the registration results.

[0034] After the registration is completed, the system automatically calculates the reprojection error residual of each ground control point. The calculation method is: the relationship between the ground coordinates (X, Y) and the image coordinates (x, y) is expressed as: ;in , is the transformation parameter, which is 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 parameter is The calculation expression is: ; Where: A is the design matrix (including the coordinates of the control points); is the unit weight error (which can be estimated as the mean of the residual sum of squares), is the principal term of the covariance of the normal equation.

[0035] The control point coordinates The error is propagated to the image point , calculate the covariance matrix of the reprojection error , the expression is: ;in: is The Jacobian matrix of the partial derivatives at , where T is the matrix transpose.

[0036] Calculate the error ellipse parameters and residual quantization indicators, by Extract the main characteristic indicators of the reprojection error residual: main direction error (Maximum axis): ; Minor direction error (minimum axis): ; Direction angle (error ellipse direction): can be obtained from the eigenvector. The reprojection error residual is obtained by weighted summing of the main direction error and the secondary direction error under all direction angles.

[0037] Set a reasonable error tolerance (such as <1.0m); if the proportion of points in complex terrain areas where the reprojection error residual is greater than the error tolerance exceeds the threshold (such as >20%), it is determined that the registration accuracy of the area is insufficient and recalibration or optimization of the control point distribution is required.

[0038] The three-dimensional surface roughness value and the reprojection error residual are dimensionless and normalized so that they are both between [0,1]. The multi-source registration error conflict score of each region is calculated based on the normalized three-dimensional surface roughness value and the reprojection error residual.

[0039] For example, the present invention can use the following multi-source data registration consistency evaluation model formula to calculate the multi-source registration error conflict score value, and the calculation expression is: ; In the formula, is the multi-source registration error conflict score, is the normalized three-dimensional surface roughness value, is the normalized reprojection error residual, is the normalized surface 3D roughness value and the weight coefficient of the reprojection error residual (which can be optimized based on experimental experience or machine learning), and Both are greater than 0.

[0040] The score of each ground control point Spatial interpolation is a continuous heat map layer. The inverse distance weighted method is used to obtain a registration conflict risk heat map, and a conflict score threshold (such as 0.7) is set. The continuous areas where the multi-source registration error conflict score value exceeds the conflict score threshold are extracted as potential conflict areas and output as a vector surface layer.

[0041] For the identified registration conflict areas, fine-tuned corrections are made to the local coordinates and elevations to eliminate spatial positioning deviations caused by differences in data sources or complex terrain, and to ensure the accuracy of the subsequent greening vegetation operation boundaries and configuration areas.

[0042] Utilize the output conflict area vector surface layer to extract the area boundary; encrypt and deploy auxiliary GCPs (ground control points) or feature matching points in the area, which can be done by: field RTK measurement; automatic matching of image features (such as SIFT, ORB); auxiliary comparison of existing layers.

[0043] The deformation control algorithm is used to fine-tune the coordinate system. The algorithm selected is Thin Plate Spline (TPS) surface transformation. Specifically, let the point in the original image be , the target (real coordinates) is ; Fitting a smooth transformation function by minimizing the energy function , so that the point pairs before and after deformation coincide as much as possible: ;in: is the radial basis function, is the control coefficient, which controls the deformation strength, m is the total number of points in the original image, Controls overall translation and rotation.

[0044] In the same area, extract DEM elevation data; compare the original elevation with the measured elevation z of the auxiliary control point, and calculate the elevation residual; use the spline interpolation method to generate an elevation error correction grid in the area; export the orthophoto (DOM), DEM, and vector boundary map (vegetation boundary, work area) obtained by elevation interpolation correction into a unified projection coordinate system (such as CGCS2000 / UTM) format; output format: GeoTIFF (raster), Shapefile (vector), GeoJSON (Web service), etc.

[0045] Import the corrected data into a GIS platform (such as ArcGIS / QGIS) and replace the old layers; update the following layer contents: Vegetation distribution map (such as distribution of shrubs, trees, and lawn areas); operation boundary map (such as pruning areas, replanting areas, sprinkler belts, etc.); risk warning map (such as mismatch areas and high error area markings).

[0046] Using the updated layer and combining it with the attribute database, the following functions can be achieved: Vegetation screening suggestions: combine terrain and spatial accuracy to output the optimal vegetation type; greening configuration optimization: output vegetation matching and density layout plan; maintenance operation precision control: combine GPS navigation to control the operation path and equipment operation boundary; error tracing and correction record management: each calibration data is archived for easy tracing and analysis.

[0047] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for screening and configuring highway ecological greening vegetation, characterized in that: include: Collect multi-source data along the highway, including remote sensing images, drone images, ground RTK measurement data and digital elevation models, and perform format conversion and preliminary coordinate unification; The three-dimensional roughness characteristics of the surface are obtained based on the digital elevation model to identify areas with complex terrain. Based on the identification results, the remote sensing or UAV images are geo-referenced to obtain the residual characteristics of the reprojection error in the complex terrain area to evaluate the accuracy of spatial registration. Perform spatial overlay analysis on the three-dimensional surface roughness characteristics and reprojection error residual characteristics in complex terrain areas, establish a multi-source data registration consistency evaluation model, and identify potential conflict areas where coordinate system errors are concentrated; The local coordinate system and elevation correction are carried out in the potential conflict area, and the deformation control algorithm is used for fine calibration. The corrected data is imported into the GIS system to update the vegetation distribution and operation boundaries, so as to realize the precise management of vegetation screening, configuration and maintenance operations.

2. The method for screening and configuring highway ecological greening vegetation according to claim 1 is characterized by: In each window, the three-dimensional roughness value of the surface is calculated. Specifically, a sliding window of size N×N is set for the input digital elevation model image; the central pixel of the window is taken as the analysis target, and the elevation value and its corresponding spatial coordinates are extracted in its neighborhood; in each window, a local surface is fitted using the least squares method, and the fitting model is in the form of a quadratic surface. For each point in the window, the difference between its actual elevation and the elevation of the fitted surface is calculated, and the root mean square value of the elevation residual of all points in the window is calculated as the three-dimensional surface roughness value of the pixel point.

3. The method for screening and configuring highway ecological greening vegetation according to claim 2 is characterized by: The three-dimensional surface roughness value calculated by each sliding window is assigned to the central pixel of the window to form a new three-dimensional surface roughness index layer, and the roughness threshold is set; the area exceeding the threshold is identified as a complex terrain area.

4. The method for screening and configuring highway ecological greening vegetation according to claim 3 is characterized by: After the registration is completed, the reprojection error residual of each ground control point is calculated. The calculation method is: The relationship between the ground coordinates (X, Y) and the image coordinates (x, y) is expressed as: ;in , is the transformation parameter, which is 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 parameter is The calculation expression is: ; Where: A is the design matrix; is the unit weight error, is the principal term of the covariance of the normal equation; The control point coordinates The error is propagated to the image point , calculate the covariance matrix of the reprojection error , the expression is: ;in: is The Jacobian matrix of partial derivatives at , T is the matrix transpose; According to the covariance matrix of the reprojection error, the main direction error of the reprojection error residual is extracted , secondary direction error And the direction angle, the weighted sum of the main direction error and the secondary direction error under all direction angles is calculated to obtain the reprojection error residual.

5. The method for screening and configuring highway ecological greening vegetation according to claim 4 is characterized by: The three-dimensional surface roughness value and the reprojection error residual are dimensionless and normalized so that they are both between [0,1]. The multi-source registration error conflict score of each region is calculated based on the normalized three-dimensional surface roughness value and the reprojection error residual.

6. The method for screening and configuring highway ecological greening vegetation according to claim 5 is characterized by: The score of each ground control point Spatial interpolation is a continuous heat map layer. The inverse distance weighted method is used to obtain a registration conflict risk heat map and set a conflict score threshold. The continuous areas where the multi-source registration error conflict score value exceeds the conflict score threshold are extracted as potential conflict areas and output as a vector surface layer.

7. The method for screening and configuring highway ecological greening vegetation according to claim 6 is characterized by: The output conflict area vector surface layer is used to extract the area boundary, and auxiliary ground control points are densely arranged in the area. The coordinate system is adjusted using the deformation control algorithm. Specifically, the points in the original image are set as , the real coordinates are , so that the point pairs before and after deformation coincide; In the same area, extract DEM elevation data, compare the original elevation with the measured elevation of the auxiliary control points, and calculate the elevation residual; use the spline interpolation method to generate an elevation residual correction grid in the area; export the orthophoto and vector boundary map obtained after elevation interpolation correction into a unified projection coordinate system format; Import the corrected data into the GIS platform and replace the old layer; Update vegetation distribution maps, work boundary maps and risk warning maps, and use the updated layers to perform vegetation screening and management.

Images