A method for extracting negative terrain in karst mountainous areas based on digital elevation model

Abstract

The present invention relates to a method for extracting negative terrain in a karst mountain area based on a digital elevation model, comprising the following steps: obtaining the digital elevation model standby data of the target area through data collection and processing; pre-processing the digital elevation model standby data of the target area to establish a fine digital elevation model; determining the negative terrain area through terrain digital analysis methods such as filling depressions, raster calculation, vector calculation, and condition judgment; based on the determined negative terrain area, combining the topological structure relationship of the negative terrain, using closed contour lines to accurately extract the negative terrain boundary; finally, analyzing the negative terrain extraction results through remote sensing image data overlay, and verifying the accuracy of the extraction results by visual evaluation. The present invention identifies and extracts the negative terrain in the karst mountain area based on the digital elevation model, realizes the accurate positioning of the negative terrain in the karst mountain area and the accurate extraction of the boundary, and provides accurate data for the development and utilization of negative terrain engineering in the karst mountain area.

Description

Technical Field

[0001] The invention relates to the technical field of negative terrain data recognition in karst mountainous areas, and in particular to a method for extracting negative terrain in karst mountainous areas based on a digital elevation model. Background Art

[0002] Negative topography refers to the terrain in the area of ​​neotectonic subsidence or relatively lower than the adjacent area. For example, plains and basins are both negative topography, and the Hebei Plain is a negative topography that receives huge accumulations. For example, various sedimentary deposits such as oil, coal, bauxite, iron, peat, various salts, and manganese nodules are mostly formed in negative topography such as basins, depressions, valleys, and ocean basins.

[0003] Southwest China is one of the largest and most concentrated karst landform contiguous areas in the world, with a wide distribution of landform types represented by various negative landforms. They not only have high research value in scientific fields such as geography and geology, but also because the amount of excavation in negative landforms is small, a large amount of excavation and support investment can be saved, and they can be used as ideal places for new landscapes, water conservancy replenishment projects, slag storage, pumped storage power stations, and astronomical science and technology infrastructure. Especially with the increasing scarcity of land resources, the development and utilization of negative landforms has become an inevitable choice, and there is a huge market demand.

[0004] However, existing technologies for extracting negative terrain either rely on low-resolution topographic maps and visual interpretation of aerial photography data, conduct manual on-site inspections, and then manually draw the distribution of negative terrain. This method is time-consuming, labor-intensive, and inefficient for extracting negative terrain over a large area; or use remote sensing data to extract negative terrain through generalized models or hydrological models. However, the number of negative terrain extracted by this method is far lower than the actual distribution. More importantly, the boundaries of negative terrain extracted by this method are not accurate enough.

[0005] As the most important carrier of terrain data and geoscientific analysis, Digital Elevation Model (DEM) provides rich geographic information in the process of quantitative geomorphological research, improving the recognition accuracy and analysis speed of geomorphological research. This method is mainly based on DEM data, formulates landform classification standards, extracts landform-related terrain factors from DEM data, and obtains the landform of the study area based on the importance and information content of the extracted landform.

[0006] Therefore, in order to improve the accurate positioning and accurate boundary extraction of negative terrain in karst mountainous areas, it is of great significance to propose a method for extracting negative terrain in karst mountainous areas based on digital elevation model. The present invention solves the problems of complex process, low efficiency and poor precision in extracting negative terrain in karst mountainous areas in the prior art, realizes accurate positioning of negative terrain in karst mountainous areas and accurate extraction of boundaries, and provides accurate data for the development and utilization of negative terrain engineering in karst mountainous areas. Summary of the invention

[0007] The purpose of the present invention is to provide a method for extracting negative terrain in karst mountainous areas based on a digital elevation model, so as to achieve accurate positioning of negative terrain in karst mountainous areas and precise extraction of boundaries.

[0008] A method for extracting negative terrain in karst mountainous areas based on a digital elevation model comprises the following steps:

[0009] S100, obtaining digital elevation model data of the target area;

[0010] S200, preprocessing the digital elevation model data of the target area;

[0011] S300, determining a negative terrain area through terrain digital analysis;

[0012] S400, extracting negative terrain boundaries based on closed contour lines;

[0013] S500: verify the negative terrain extraction result.

[0014] Furthermore, the S100 specifically includes the following steps:

[0015] S101, determining the target karst mountain area as the data boundary of the digital elevation model according to the project needs, and establishing boundary vector data;

[0016] S102. Obtain digital elevation model data with an accuracy of meters or above through public website information.

[0017] Furthermore, the step S200 specifically includes the following steps:

[0018] S201, cutting the collected high-precision digital elevation model data to obtain digital elevation model data to be used for extracting negative terrain in the target area;

[0019] S202: performing mean smoothing preprocessing on the digital elevation model data to be used in the target area, and rasterizing the digital elevation model of the target area.

[0020] Furthermore, in S202, the domain analysis type is rectangle, the domain is set as a 3×3 pixel window, and the statistical type is mean.

[0021] Furthermore, the S300 specifically includes the following steps:

[0022] S301, in the digital elevation model of the target area, by performing spatial analysis on the rasterized digital elevation model, traversing and scanning the pixel values ​​between each central grid unit and the adjacent grid units, determining the depression point by judging the size of the central grid unit and the surrounding pixel values, if the surrounding pixel values ​​are all higher than the central grid unit pixel value, it is judged as a depression point, and all pixels of the digital elevation model of the target area are traversed and scanned in this way;

[0023] S302, filling all the sunken points detected in the grid to remove the negative terrain in the data, and assigning the lowest pixel value around the sunken point to the filled grid to obtain a digital elevation model without sunken points;

[0024] S303, obtaining the high-precision digital elevation model of S202 and the non-sag digital elevation model of S302, raster calculating the difference between the non-sag digital elevation model and the high-precision digital elevation model, and obtaining the relative depth pixel value of the negative terrain, wherein the pixel value outside the negative terrain raster area is 0;

[0025] S304, performing grid value assignment calculation, converting the acquired negative terrain into a binary image, assigning 1 to pixels with values ​​greater than 0 and 0 to pixels with values ​​equal to 0, thereby obtaining a negative terrain grid area;

[0026] S305, merging regions, scanning in order from left to right and from top to bottom, the first region scanned receives a value of 1, the second region receives a value of 2, and so on, until all regions have been assigned values, and each negative terrain grid region is assigned a unique number;

[0027] S306, converting the negative terrain grid area into a vector surface to obtain a vector map of the negative terrain area, and obtaining the area value of each negative terrain area by area calculation;

[0028] S307, screening negative terrain areas with an area greater than a set value by setting conditions;

[0029] S308. Perform buffer analysis on the negative terrain areas screened by area.

[0030] Furthermore, the buffer distance in S308 is greater than the pixel value of the digital elevation model data collected in S102. Specifically, the buffer distance is greater than the pixel value of the digital elevation model data collected in S102 by 3% to 6%.

[0031] Preferably, the buffer distance in S308 is greater than 4% of the pixel value of the digital elevation model data collected in S102.

[0032] Furthermore, the step S400 specifically includes the following steps:

[0033] S401, obtaining the digital elevation model of S202 and the negative terrain area of ​​the buffer zone whose area has been screened in S308, and clipping to obtain the raster data of the negative terrain area of ​​the buffer zone;

[0034] S402, generating contour lines from the extracted raster data;

[0035] S403, converting the generated contour lines into contour vectors according to the elements, and obtaining the contour surface of a closed loop;

[0036] S404, aggregating the specified attribute elements together by fusion to form a continuous isosurface, where all negative terrain areas are fused into one surface with the same number;

[0037] S405, obtaining the closed trap contour surface of S403 and the fused contour surface of S404, selecting by position, and selecting the outermost contour surface circle of the trap contour surface according to the colinear characteristics of the surface elements in the trap contour surface and the surface elements in the fused contour surface;

[0038] S406, according to the element-to-line conversion, the outermost contour surface circle of the selected trap contour surface is converted into a vector line, and two closed contour lines of the inner line and the outer line of the outermost contour surface circle are obtained;

[0039] S407, obtaining two closed contour lines, the inner line and the outer line of the fused contour surface of S404 and the outermost contour surface circle of S406, selecting by position, and selecting the outermost closed contour line according to the feature that the surface element in the fused contour surface is colinear with the outer closed contour line element of S406;

[0040] S408, select the outermost closed contour line and convert it into a surface by element, generate a negative terrain list with unique number, independent operation and precise boundaries, and form a negative terrain resource distribution map of the target area.

[0041] 1. Further, the S402 generates contour lines from the extracted raster data, and the contour line spacing is 0.1-0.2 times the accuracy of the digital elevation model.

[0042] Furthermore, the attribute element specified in S404 is the isosurface generated in S403.

[0043] Furthermore, the step S500 specifically includes the following steps:

[0044] S501, collecting satellite remote sensing image data of the target area;

[0045] S502, superimposing the negative terrain vector map extracted in S408 with the satellite remote sensing image data, setting the negative terrain vector map to be semi-transparent, and preliminarily evaluating the accuracy of the negative terrain position and boundary through visual means.

[0046] The beneficial effects of the present invention are as follows: the present invention obtains the standby data of the digital elevation model of the target area through data collection and processing; pre-processes the standby digital elevation model data of the target area to establish a fine digital elevation model; then determines the negative terrain area through terrain digital analysis methods such as depression filling, raster calculation, vector calculation, condition judgment, etc.; on the basis of the determined negative terrain area, combined with the topological structure relationship of the negative terrain, the negative terrain boundary is accurately extracted using closed contour lines; finally, the negative terrain extraction result is superimposed by remote sensing image data, and the accuracy of the extraction result is verified by visual evaluation, so as to realize the precise positioning of the negative terrain in the karst mountainous area and the precise extraction of the boundary. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a schematic diagram of a flow chart of an embodiment of the present invention;

[0048] Figure 2 A schematic diagram of a digital elevation model of a target area according to an embodiment of the present invention;

[0049] Figure 3 A schematic diagram of focus statistical analysis according to an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of depression filling analysis according to an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of negative terrain contour lines according to an embodiment of the present invention;

[0052] Figure 6 This is a schematic diagram of a negative terrain closed isosurface according to an embodiment of the present invention;

[0053] Figure 7 This is a schematic diagram of a negative terrain boundary according to an embodiment of the present invention;

[0054] Figure 8 Schematic diagram of negative terrain visual assessment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. The exemplary embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0056] Example

[0057] A negative terrain extraction method for karst mountainous areas based on digital elevation model, the process diagram is as follows Figure 1 , including the following steps:

[0058] S100, obtaining digital elevation model data of the target area, specifically comprising the following steps:

[0059] S101. A karst mountain area where the project is to be constructed is used as the data boundary of the digital elevation model, and boundary vector data is established, which is 16 km long and 10 km wide;

[0060] S102, select existing publicly available data that can be downloaded over a large area, select regional digital elevation model data with a resolution of 12.5m, and obtain digital elevation model data of a karst mountain area where engineering construction is planned in S101. In this embodiment, the data is downloaded from the website of NASA.

[0061] S200, preprocessing the target area digital elevation model data, specifically including the following steps:

[0062] S201, in the ArcGIS system, the collected high-precision digital elevation model data is clipped according to the flooding extraction method to obtain the digital elevation model data for extracting negative terrain in the target area, such as Figure 2 , the maximum elevation of the target area digital elevation model obtained in the embodiment of the present invention is 1485m and the minimum elevation is 192m;

[0063] S202, applying the focus statistics method in the ArcGIS system to perform mean smoothing preprocessing on the digital elevation model data to be used in the target area, the domain analysis type is rectangular, the domain is set to a 3×3 pixel window, the statistics type is mean, and the digital elevation model of the target area is rasterized. Figure 3 It is the process and result of the mean smoothing of the digital elevation model in the example of the present invention.

[0064] S300, determining the negative terrain area through terrain digital analysis, specifically comprising the following steps:

[0065] S301, in the digital elevation model of the target area, by performing spatial analysis on the rasterized digital elevation model, traversing and scanning the pixel values ​​between each central grid unit and the adjacent grid units, determining the depression point by judging the size of the central grid unit and the surrounding pixel values, if the surrounding pixel values ​​are all higher than the central grid unit pixel value, it is judged as a depression point, and all pixels of the digital elevation model of the target area are traversed and scanned in this way;

[0066] S302, filling all the sunken points detected in the grid to remove the negative terrain in the data, and assigning the lowest pixel value around the sunken point to the filled grid to obtain a digital elevation model without sunken points;

[0067] S303, obtaining the high-precision digital elevation model of S202 and the non-sag digital elevation model of S302, raster calculating the difference between the non-sag digital elevation model and the high-precision digital elevation model, and obtaining the relative depth pixel value of the negative terrain, wherein the pixel value outside the negative terrain raster area is 0;

[0068] S304, performing grid value assignment calculation, converting the acquired negative terrain into a binary image, assigning 1 to pixels with values ​​greater than 0 and 0 to pixels with values ​​equal to 0, thereby obtaining a negative terrain grid area;

[0069] S305, merging regions, scanning in order from left to right and from top to bottom, the first region scanned receives a value of 1, the second region receives a value of 2, and so on, until all regions have been assigned values, and each negative terrain grid region is assigned a unique number;

[0070] S306, converting the negative terrain grid area into a vector surface to obtain a vector map of the negative terrain area, and obtaining the area value of each negative terrain area by area calculation;

[0071] S307, by setting conditions to judge and filter negative terrain areas with an area greater than a set value, since depressions, tiankengs, sinkholes, funnels, troughs, basins, mine pits and other negative terrains have similar topological structures, the extraction results also include the above-mentioned terrains and landforms. Considering that small-sized negative terrains have little practical significance, this embodiment uses negative terrain areas greater than 100m 2 As the setting value of the area screening condition;

[0072] S308, performing a buffer analysis on the negative terrain area screened by area, the buffer distance in S308 being greater than the pixel value of the digital elevation model data collected in S102. This embodiment adopts a 12.5m digital elevation model, so the buffer distance is set to 13m.

[0073] S400, extracting negative terrain boundaries based on closed contour lines, specifically includes the following steps:

[0074] S401, obtaining the digital elevation model of S202 and the negative terrain area of ​​the buffer zone whose area has been screened in S308, and clipping the negative terrain area of ​​the buffer zone according to the flooded film extraction method to obtain the raster data;

[0075] S402, generate contour lines from the grid data extracted by flooding film, such as Figure 5 As shown, in this embodiment, the contour line spacing is set to 2m, and the contour lines are generated using bilinear interpolation;

[0076] S403, convert the generated contour lines into contour surface vectors according to the elements, and obtain the contour surface of the closed loop, such as Figure 6 As shown;

[0077] S404, the specified attribute elements are aggregated together by fusion to form a continuous isosurface, all negative terrain areas are fused into one surface with the same number, the fusion input element is the isosurface generated in S403, and the isosurface number is selected for fusion. Since all negative terrain areas have the same number at this time, an overall vector surface will be obtained after fusion;

[0078] S405, obtaining the closed trap contour surface of S403 and the fused contour surface of S404, selecting by position, and selecting the outermost contour surface circle of the trap contour surface according to the colinear characteristics of the surface elements in the trap contour surface and the surface elements in the fused contour surface. At this time, the selected surface is a ring surface composed of an inner line and an outer line;

[0079] S406, according to the element-to-line conversion, the outermost contour surface circle of the selected trap contour surface is converted into a vector line, and two closed contour lines of the inner line and the outer line of the outermost contour surface circle are obtained;

[0080] S407, obtain the inner and outer closed contour lines of the fused contour surface of S404 and the outermost contour surface circle of S406, select by position, and select the outermost closed contour line according to the feature that the surface element in the fused contour surface is collinear with the outer closed contour line element of S406. Because at this time, only the outermost contour lines of the fused contour surface and the closed trap contour surface of S403 are coincident and collinear, so only the outermost contour lines of the trap contour surface can be selected, which are also the negative terrain boundaries that need to be obtained in the example of the present invention, such as Figure 7 As shown;

[0081] S408, select the outermost closed contour line and convert it into a surface by element, generate a negative terrain list with unique number, independent operation and precise boundaries, and form a negative terrain resource distribution map of the target area.

[0082] S500, verifying the negative terrain extraction result, specifically including the following steps:

[0083] S501, collecting satellite remote sensing image data of the target area. The image data collected in this embodiment is from Tiandi Map online remote sensing image data;

[0084] S502, superimposing the negative terrain vector map extracted in S408 with the satellite remote sensing image data, setting the negative terrain vector map to be semi-transparent, and preliminarily evaluating the accuracy of the negative terrain position and boundary through visual means.

[0085] Although the present invention has been described in detail above by general explanation and specific implementation methods, it is obvious to those skilled in the art that some modifications or improvements can be made on the basis of the present invention. Therefore, these modifications or improvements made on the basis of not departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for extracting negative terrain in karst mountainous areas based on digital elevation model, characterized in that: The following steps are involved: S100, obtaining digital elevation model data of the target area; S200, preprocessing the digital elevation model data of the target area; S300, determining the negative terrain area through terrain digital analysis, specifically comprising the following steps: S301, in the digital elevation model of the target area, by performing spatial analysis on the rasterized digital elevation model, traversing and scanning the pixel values ​​between each central grid unit and the adjacent grid units, determining the depression point by judging the size of the central grid unit and the surrounding pixel values, if the surrounding pixel values ​​are all higher than the central grid unit pixel value, it is judged as a depression point, and all pixels of the digital elevation model of the target area are traversed and scanned in this way; S302, filling all the sunken points detected in the grid to remove the negative terrain in the data, and assigning the lowest pixel value around the sunken point to the filled grid to obtain a digital elevation model without sunken points; S303, obtaining the high-precision digital elevation model of S202 and the non-sag digital elevation model of S302, raster calculating the difference between the non-sag digital elevation model and the high-precision digital elevation model, and obtaining the relative depth pixel value of the negative terrain, wherein the pixel value outside the negative terrain raster area is 0; S304, performing grid value assignment calculation, converting the acquired negative terrain into a binary image, assigning 1 to pixels with values ​​greater than 0 and 0 to pixels with values ​​equal to 0, thereby obtaining a negative terrain grid area; S305, merging regions, scanning in order from left to right and from top to bottom, the first region scanned receives a value of 1, the second region receives a value of 2, and so on, until all regions have been assigned values, and each negative terrain grid region is assigned a unique number; S306, converting the negative terrain grid area into a vector surface to obtain a vector map of the negative terrain area, and obtaining the area value of each negative terrain area by area calculation; S307, screening negative terrain areas with an area greater than a set value by setting conditions; S308, performing buffer zone analysis on the negative terrain area screened by area; S400, extracting negative terrain boundaries based on closed contour lines, specifically includes the following steps: S401, obtaining the digital elevation model of S202 and the negative terrain area of ​​the buffer zone whose area has been screened in S308, and clipping to obtain the raster data of the negative terrain area of ​​the buffer zone; S402, generating contour lines from the extracted raster data; S403, converting the generated contour lines into contour surface vectors according to the elements, and obtaining the contour surface of the closed loop; S404, aggregating the isosurfaces together by fusion to form a continuous isosurface, and all negative terrain areas are fused into one surface with the same number; S405, obtaining the closed trap contour surface of S403 and the fused contour surface of S404, selecting by position, and selecting the outermost contour surface circle of the trap contour surface according to the colinear characteristics of the surface elements in the trap contour surface and the surface elements in the fused contour surface; S406, according to the element-to-line conversion, the outermost contour surface circle of the selected trap contour surface is converted into a vector line, and two closed contour lines of the inner line and the outer line of the outermost contour surface circle are obtained; S407, obtaining two closed contour lines, the inner line and the outer line of the fused contour surface of S404 and the outermost contour surface circle of S406, selecting by position, and selecting the outermost closed contour line according to the feature that the surface element in the fused contour surface is colinear with the outer closed contour line element of S406; S408, selecting the outermost closed contour line and converting it into a surface by element, generating a negative terrain list with unique number, independent operation and precise boundaries, and forming a negative terrain resource distribution map of the target area; S500: verify the negative terrain extraction result.

2. The method for extracting negative terrain in karst mountainous areas based on digital elevation model according to claim 1, characterized in that: The S100 specifically includes the following steps: S101, determining the target karst mountain area as the data boundary of the digital elevation model, and establishing boundary vector data; S102. Obtain digital elevation model data with an accuracy of meters or above through public website information.

3. The method for extracting negative terrain in karst mountainous areas based on digital elevation model according to claim 1, characterized in that: The S200 specifically includes the following steps: S201, cutting the collected high-precision digital elevation model data to obtain digital elevation model data to be used for extracting negative terrain in the target area; S202: performing mean smoothing preprocessing on the digital elevation model data to be used in the target area, and rasterizing the digital elevation model of the target area.

4. The method for extracting negative terrain in karst mountainous areas based on digital elevation model according to claim 3 is characterized in that: In S202, the domain analysis type is rectangle, the domain is set as a 3×3 pixel window, and the statistical type is mean.

5. The method for extracting negative terrain in karst mountainous areas based on digital elevation model according to claim 1, characterized in that: The buffer distance in S308 is 3% to 6% greater than the pixel value of the digital elevation model data collected in S102.

6. The method for extracting negative terrain in karst mountainous areas based on digital elevation model according to claim 1, characterized in that: The S402 generates contour lines from the extracted raster data, and the contour line spacing is 0.1-0.2 times the accuracy of the digital elevation model.

7. The method for extracting negative terrain in karst mountainous areas based on digital elevation model according to claim 5, characterized in that: The buffer distance in S308 is greater than the pixel value of the digital elevation model data collected in S102 by 4%.

8. The method for extracting negative terrain in karst mountainous areas based on digital elevation model according to claim 1, characterized in that: The S500 specifically includes the following steps: S501, collecting satellite remote sensing image data of the target area; S502, superimposing the negative terrain vector map obtained in S408 with the satellite remote sensing image data, setting the negative terrain vector map to be semi-transparent, and visually evaluating the accuracy of the negative terrain position and boundary.

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