Method for extracting large-scale loess tableland surface based on ridge points

Abstract

The present invention discloses a method for extracting large-scale loess tablelands based on ridge points, including: S1, planar curvature calculation: Based on the planar curvature of the original DEM of the study area and the planar curvature of the inverted DEM, calculate and obtain the unbiased planar curvature of the study area; S2, ridge point extraction: By setting threshold conditions for the unbiased planar curvature and local positive and negative terrains of the study area, screen out the ridge points of the study area; S3, potential tableland extraction: According to the local undulation degree of the study area, screen out potential tablelands by setting an undulation threshold; S4, formal tableland extraction: According to the number of ridge points inside each potential tableland, screen out the potential tablelands containing a preset number of ridge points and use them as formal tablelands. The advantages are that the present invention can accurately and efficiently extract loess tablelands, is applicable to the extraction of large-scale loess tablelands, and provides strong support for geomorphic analysis, land use planning, and ecological environment protection in the Loess Plateau region.

Description

Technical Field

[0001] The present invention relates to the technical field of loess tableland extraction, and particularly to a method for extracting large-scale loess tablelands based on ridge points. Background Art

[0002] A loess tableland is a gully area composed of a tableland surface, tableland slopes, and valleys under the influence of tectonic movements, erosion, deposition, and human activities. The loess tableland surface is the most precious arable, agricultural, and livable land resource on the Loess Plateau. Gully erosion causes fragmentation of the tableland surface, seriously affecting land use. Accurately extracting the tableland is crucial for scientific research and protection of the Loess Plateau.

[0003] Currently, the main methods for tableland extraction include field measurement, remote sensing image extraction, and DEM extraction. Although field measurement can obtain relatively accurate tableland boundaries, its accuracy is subject to the subjective judgment of the operator, and the efficiency is low. The remote sensing image method can accurately identify the tableland edge through image segmentation and feature recognition, but it is not very suitable for large-area feature extraction. The DEM method first determines the gully line based on slope change points to identify the tableland. However, in practical applications, there are certain challenges in ensuring the effectiveness and coherence of gully line feature points. At the same time, most existing studies are based on the gully lines of local small watersheds for extraction, and different geomorphic units need to be redefined for different geomorphic types. To solve this problem, scientific researchers are urgently in need of developing a technical means that can quickly, simply, and effectively extract large-scale loess tablelands and is applicable to different regions. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for extracting large-scale loess tablelands based on ridge points, so as to solve the foregoing problems existing in the prior art.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] A method for extracting large-scale loess tablelands based on ridge points includes the following steps:

[0007] S1. Planar curvature calculation: Based on the planar curvature of the original DEM of the study area and the planar curvature of the inverted DEM, calculate and obtain the unbiased planar curvature of the study area;

[0008] S2. Ridge point extraction: Calculate and obtain the local positive and negative terrains of the study area according to the local average elevation of the study area. By setting threshold conditions for the unbiased planar curvature and local positive and negative terrains of the study area, screen out the ridge points of the study area;

[0009] S3. Potential tableland extraction: According to the local undulation degree of the study area, screen out potential tablelands by setting an undulation degree threshold;

[0010] S4. Formal plateau surface extraction: According to the number of ridge points inside each potential plateau surface, the potential plateau surfaces containing a preset number of ridge points are screened out and used as formal plateau surfaces.

[0011] Preferably, step S1 is specifically as follows: based on the original DEM of the study area, the plane curvature of the original DEM of the study area and the plane curvature of the inverted DEM are calculated respectively; the plane curvature of the original DEM of the study area is added to the plane curvature of the inverted DEM and the absolute value of the difference between the two is subtracted, and then the average value is calculated to obtain the unbiased plane curvature of the study area.

[0012] Preferably, the planar curvature of the original DEM is calculated as follows:

[0013] Based on the original DEM of the study area, the downslope direction with the largest rate of change in value from each pixel to other adjacent pixels, i.e., the slope aspect, is calculated; based on the slope aspect, the maximum rate of change in the slope aspect of each pixel from the pixel to its adjacent pixels, i.e., the plane curvature of the original DEM of the study area, is calculated.

[0014] Preferably, the slope aspect is represented by positive degrees between 0 and 359.9 degrees, measured clockwise with north as the reference direction.

[0015] Preferably, the planar curvature of the inverted DEM is calculated as follows:

[0016] Based on the original DEM of the study area, the maximum elevation of the study area is extracted, and the difference between the maximum elevation of the study area and the original DEM of the study area is calculated to obtain the inverted terrain. Referring to the calculation method of the plane curvature of the original DEM, the plane curvature of the inverted DEM of the study area is calculated.

[0017] Preferably, step S2 is specifically as follows: based on the original DEM of the study area, the local average elevation of each pixel is calculated using a moving window method; the local average elevation value of each pixel is subtracted from the original DEM of the study area, and the local positive and negative terrain of each pixel is obtained according to the positive or negative value of the calculation result; threshold conditions are set for the unbiased plane curvature of the study area and the local positive and negative terrain of each pixel, and pixels with positive terrain and plane curvature greater than a preset threshold are screened out, that is, the ridge points of the study area are obtained.

[0018] Preferably, the local plane elevation is calculated as follows:

[0019] The moving window method is used to calculate the average value of all pixels in the specified neighborhood for each pixel of the original DEM of the study area to obtain the local average elevation of each pixel.

[0020] Preferably, step S3 specifically includes calculating the elevation difference between the highest point and the lowest point in the local area where each pixel is located as the local relief of the pixel; setting a relief threshold to screen out flat areas with small relief as potential plateau surfaces.

[0021] Preferably, step S4 is specifically as follows: using a spatial statistics tool to count the number of ridge points in the potential tableland, setting a threshold for the number of ridge points, screening out the potential tablelands containing a preset number of ridge points, and taking them as the formal tablelands.

[0022] Preferably, use a raster-to-vector tool to convert the screened formal tableland in raster format into a formal tableland in vector format, which is convenient for subsequent spatial analysis and processing.

[0023] The beneficial effects of the present invention are as follows: 1. The present invention calculates the plane curvature based on the original terrain and the inverted terrain of the study area, which can reduce the deviation of terrain features and obtain a more comprehensive and unbiased description of terrain features; 2. The present invention obtains the local positive terrain based on the original terrain and the local average elevation, and combines the plane curvature to obtain the ridge points of the study area, ensuring that they are located at the terrain high points and have obvious convex features, improving the accuracy of ridge point recognition. 3. The present invention identifies flat areas as potential tablelands according to the local terrain undulation, which conforms to the terrain characteristics of the loess tablelands. 4. The present invention screens out the potential tablelands containing a certain number of ridge points as the formal tablelands by counting the number of ridge points in each potential tableland. These areas are not only flat but also contain a certain number of ridge points, ensuring the reliability and scientificity of their being used as loess tablelands. 5. The present invention can accurately and efficiently extract the loess tablelands, is applicable to the extraction of large-scale loess tablelands, and provides strong support for the geomorphic analysis, land use planning and ecological environment protection in the loess plateau area. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is the technical roadmap of the extraction method in the embodiment of the present invention;

[0025] Figure 2 is the extraction result of the loess tablelands in Qingyang City (the whole city) in the embodiment of the present invention;

[0026] Figure 3 is the extraction result of the potential tablelands of the loess tablelands in Qingyang City (partial) in the embodiment of the present invention;

[0027] Figure 4 is the extraction result of the formal tablelands of the loess tablelands in Qingyang City (partial) in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0029] Embodiment 1

[0030] AsFigure 1 As shown, in this embodiment, a method for extracting a large-scale loess tableland surface based on ridge points is provided, which specifically includes the following four parts:

[0031] I. Planar curvature calculation

[0032] Based on the planar curvature of the original DEM in the study area and the planar curvature of the inverted DEM, the unbiased planar curvature of the study area is calculated. The specific contents are as follows:

[0033] 1.1. Calculate the planar curvature of the original terrain

[0034] First, calculate the downhill direction with the largest rate of change of values in the direction from each pixel to its adjacent pixel, that is, the aspect. The aspect is represented by positive degrees between 0 and 359.9 degrees, and is measured clockwise with north as the reference direction. Based on the aspect, calculate the maximum rate of change of the calculated value of each pixel in the aspect direction from this pixel to its adjacent pixel, which is used to identify the maximum change in the aspect within a small local area of the surface.

[0035] 1.2. Calculate the planar curvature of the inverted terrain

[0036] Extract the maximum elevation value in the study area, calculate the difference from the original DEM, and obtain the inverted terrain. Referring to the method for calculating the aspect change rate of the original terrain, calculate the aspect change rate of the inverted terrain.

[0037] 1.3. Unbiased DEM aspect change rate

[0038] To correct the aspect change rate deviation in the north slope area, calculate the sum of the aspect change rates of the original terrain and the inverted terrain minus the absolute value of their difference, and then take the average value to obtain the unbiased planar curvature of the study area. The areas with large planar curvature values are ridges or valleys.

[0039] II. Ridge point extraction

[0040] Calculate the local positive and negative terrain of the study area based on the local average elevation of the study area. By setting threshold conditions for the unbiased planar curvature and local positive and negative terrain of the study area, the ridge points of the study area are screened out. The specific contents are as follows:

[0041] 2.1. Calculate the local average elevation

[0042] Using the moving window method, calculate the average value of all pixels within the specified neighborhood range for each pixel to obtain the local average elevation of each grid. This process is equivalent to local averaging. For example, the elevations of 4 pixels are 10, 8, 6, and 4, where 10 is the peak and 4 is the valley, and the average value of the 4 pixels after averaging is 7.

[0043] 2.2. Positive and negative terrain calculation

[0044] Subtract the local average elevation value from the original DEM to obtain the local positive and negative terrain (ZFDX), which reflects the elevation change of each grid relative to its neighboring area. Among them, a positive value indicates that the elevation of the pixel is higher than the local average value, and a negative value indicates that the elevation of the pixel is lower than the local average value. For example, after subtracting the local mean value of 7 from the original DEM, the obtained results are 3, 1, -1, -3. Those greater than 0 are positive terrains, and those less than 0 are negative terrains.

[0045] 2.3 Ridge point identification

[0046] Set threshold conditions for the plane curvature and positive and negative terrain, and screen out the pixels with positive terrain and relatively large plane curvature (the plane curvature is greater than the preset threshold), then the ridge points in the study area can be obtained. The threshold conditions can be selected according to the actual situation to better meet the actual needs. For example, the threshold conditions can be set as the plane curvature is greater than 80 and the local terrain is positive.

[0047] III. Potential tableland extraction

[0048] According to the local relief degree of the study area, screen out the potential tablelands by setting the relief degree threshold. The specific content is as follows:

[0049] 1. Relief degree calculation

[0050] Calculate the elevation difference between the highest point and the lowest point in the local area where each pixel is located to obtain the relief degree.

[0051] 2. Potential tableland identification

[0052] Set the relief degree threshold and screen out the flat areas with relatively small relief degree as potential tablelands. This step is based on the assumption that the loess tablelands usually show relatively flat terrains. The relief degree threshold can be set according to the actual situation to better meet the actual needs. For example, the relief degree threshold can be set to 20.

[0053] IV. Formal tableland extraction

[0054] According to the number of ridge points inside each potential tableland, screen out the potential tablelands containing a certain number of ridge points and take them as the formal tablelands. The specific content is as follows:

[0055] 4.1 Statistics of the number of ridge points inside the potential tableland

[0056] Use the spatial analysis tool to calculate the number of ridge points inside each potential tableland.

[0057] 4.2 Formal tableland extraction

[0058] Screen out potential tablelands containing a preset number of ridge points and use them as formal tablelands. The preset number can be set according to the actual situation to better meet the actual needs. For example, the preset number can be set to 10.

[0059] 4.3. Vector conversion

[0060] Use the raster to vector tool to convert the screened formal tableland in raster format into a formal tableland in vector format for subsequent spatial analysis and processing.

[0061] Embodiment 2

[0062] As Figure 2 and Figure 3 shown, taking Qingyang City in Gansu area as an example, the method of the present invention is used to extract the loess tableland of Qingyang City, including the following contents:

[0063] I. Planar curvature calculation

[0064] 1.1. Calculate the curvature of the original terrain: In this study, Qingyang City in Gansu Province is taken as the study area, and the terrain data comes from the DEM data with a spatial resolution of 12.5m×12.5m released by USGS, and the vertical accuracy is 6m. Use the Aspect tool of ArcGIS to calculate the aspect of the original DEM, and on this basis, use the Slope tool to calculate the slope of the aspect to obtain the planar curvature of the original DEM.

[0065] 1.2. Extract the curvature of the inverted terrain: Extract the maximum value in the study area, calculate the difference from the original DEM to obtain the inverted terrain, and refer to the previous step to calculate the aspect and planar curvature of the inverted terrain.

[0066] 1.3. Calculate the unbiased planar curvature: Sum the planar curvatures of the original terrain and the inverted terrain, and then subtract the absolute value of their difference to obtain the unbiased planar curvature.

[0067] II. Ridge point extraction

[0068] 2.1. Calculate the local average elevation: Use the Focal Statistics tool of ArcGIS to calculate the local average value of each grid with a moving window of 12×12.

[0069] 2.2. Calculate the positive and negative terrain: Use the Raster Calculator tool of ArcGIS to calculate the difference between the original terrain and the local average elevation to obtain the positive and negative terrain.

[0070] 2.3. Ridge point identification: Use the Raster Calculator tool of ArcGIS, with the condition that the positive terrain and the planar curvature are greater than 80, to obtain 1,997,732 ridge points in the study area.

[0071] 3. Potential plateau extraction

[0072] 3.1. Calculation of relief: Use the Focal Statistics tool of ArcGIS to calculate the relief of the local area where each pixel is located with a 12×12 window.

[0073] 3.2. Identification of potential plateaus: Using the Raster Calculator tool of ArcGIS, we screened out areas with a relief less than 20 meters based on the relief results, and obtained 57,461 potential plateaus.

[0074] 4. Formal plateau extraction

[0075] 4.1. Statistics of the number of ridge points in potential plateaus: Use the Zonal Statistics tool of ArcGIS to conduct regional statistical analysis on the ridge points in the identified potential plateau areas and calculate the number of ridge points in each potential plateau.

[0076] 4.2. Screening out formal plateau surfaces: Using the Extract by Attributes tool in ArcGIS, potential plateau surfaces with more than 10 ridge points were screened out, and a final total of 2,709 formal plateau surfaces were obtained.

[0077] 4.3. Conversion of the formal plateau surface into vector format: Use the Raster To Polygon tool of ArcGIS to convert the formal plateau surface in raster format obtained in the previous step into vector format to facilitate subsequent spatial analysis and processing.

[0078] By adopting the above technical solution disclosed in the present invention, the following beneficial effects are obtained:

[0079] The present invention discloses a method for extracting large - scale loess tablelands based on ridge points. According to the original terrain and inverted terrain of the study area, the present invention calculates the plane curvature, which can reduce the deviation of terrain features and obtain a more comprehensive and unbiased description of terrain features. According to the original terrain and local average elevation, the present invention obtains local positive terrain, and combines with the plane curvature to obtain the ridge points of the study area, ensuring that they are located at the high points of the terrain and have obvious convex features, thus improving the accuracy of ridge point recognition. The present invention identifies flat areas as potential tablelands according to the local terrain undulation, which conforms to the terrain features of loess tablelands. The present invention screens out potential tablelands containing a certain number of ridge points as formal tablelands by counting the number of ridge points in each potential tableland. These areas are not only flat but also contain a certain number of ridge points, ensuring the reliability and scientificity of their being loess tablelands. The present invention can accurately and efficiently extract loess tablelands, is applicable to large - scale extraction of loess tablelands, and provides strong support for geomorphic analysis, land use planning and ecological environment protection in the Loess Plateau region.

[0080] The above - mentioned is only the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and retouches can be made, and these improvements and retouches should also be regarded as the protection scope of the present invention.

Claims

1. A large-scale loess plateau surface extraction method based on ridge points, characterized in that: The following steps are included: S1. Plane curvature calculation: Based on the plane curvature of the original DEM and the plane curvature of the inverted DEM of the study area, the unbiased plane curvature of the study area is calculated; S2, ridge point extraction: according to the local average elevation of the study area, the local positive and negative terrain of the study area is calculated, and the ridge points of the study area are screened out by setting threshold conditions for the unbiased plane curvature and local positive and negative terrain of the study area; S3, potential plateau surface extraction: according to the local relief of the study area, potential plateau surfaces are screened out by setting relief threshold; S4. Formal plateau surface extraction: According to the number of ridge points inside each potential plateau surface, the potential plateau surfaces containing a preset number of ridge points are screened out and used as formal plateau surfaces.

2. The large-scale loess plateau surface extraction method based on ridge points according to claim 1 is characterized in that: Step S1 specifically comprises: based on the original DEM of the study area, respectively calculating the plane curvature of the original DEM of the study area and the plane curvature of the inverted DEM; adding the plane curvature of the original DEM of the study area to the plane curvature of the inverted DEM and subtracting the absolute value of the difference between the two, and then calculating the average value to obtain the unbiased plane curvature of the study area.

3. The large-scale loess plateau extraction method based on ridge points according to claim 2 is characterized in that: The planar curvature of the original DEM is calculated as follows: Based on the original DEM of the study area, the downslope direction with the largest rate of change in value from each pixel to other adjacent pixels, i.e., the slope aspect, is calculated; based on the slope aspect, the maximum rate of change in the slope aspect of each pixel from the pixel to its adjacent pixels, i.e., the plane curvature of the original DEM of the study area, is calculated.

4. The large-scale loess plateau surface extraction method based on ridge points according to claim 3 is characterized in that: The aspect is expressed in positive degrees between 0 and 359.9 degrees, measured clockwise from north.

5. The large-scale loess plateau surface extraction method based on ridge points according to claim 3 is characterized in that: The plan curvature of the inverted DEM is calculated as follows: Based on the original DEM of the study area, the maximum elevation of the study area is extracted, and the difference between the maximum elevation of the study area and the original DEM of the study area is calculated to obtain the inverted terrain. Referring to the calculation method of the plane curvature of the original DEM, the plane curvature of the inverted DEM of the study area is calculated.

6. The large-scale loess plateau surface extraction method based on ridge points according to claim 1 is characterized in that: Step S2 specifically includes: calculating the local average elevation of each pixel using the moving window method based on the original DEM of the study area; subtracting the local average elevation value of each pixel from the original DEM of the study area, and obtaining the local positive and negative terrain of each pixel according to the positive or negative value of the calculation result; setting threshold conditions for the unbiased plane curvature of the study area and the local positive and negative terrain of each pixel, screening out pixels with positive terrain and plane curvature greater than the preset threshold, that is, obtaining the ridge points of the study area.

7. The method for extracting large-scale loess plateau surface based on ridge points according to claim 6 is characterized in that: The local plane elevation is calculated as follows: The moving window method is used to calculate the average value of all pixels in the specified neighborhood for each pixel of the original DEM of the study area to obtain the local average elevation of each pixel.

8. The method for extracting large-scale loess plateau surface based on ridge points according to claim 1 is characterized in that: Step S3 specifically includes calculating the elevation difference between the highest point and the lowest point in the local area where each pixel is located as the local relief of the pixel; setting a relief threshold to screen out flat areas with small relief as potential plateau surfaces.

9. The method for extracting large-scale loess plateau surface based on ridge points according to claim 1, characterized in that: Step S4 specifically includes using a spatial statistics tool to count the number of ridge points in the potential plateau surface, setting a ridge point number threshold, and screening out potential plateau surfaces containing a preset number of ridge points as formal plateau surfaces.

10. The method for extracting large-scale loess plateau surface based on ridge points according to claim 9, characterized in that: Use the raster-to-vector tool to convert the filtered formal plateau surface in raster format into a formal plateau surface in vector format to facilitate subsequent spatial analysis and processing.

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