An automatic classification method of topography based on local reference surface

Abstract

The application discloses a kind of topography and geomorphology automatic classification method based on local reference bottom surface, steps are as follows: based on the original DEM data of study area to generate confluence accumulation data;Based on confluence accumulation to construct river network, with river network point as the elevation control point of base terrain, construct local reference bottom surface DEM;With original DEM data subtracts local reference bottom surface DEM, obtains the surface relief of study area, according to the range of relief degree classification mountain plain, according to relief degree further divides mountain;With elevation as classification criterion to carry out global elevation classification to study area, comprehensively relief degree classification result and elevation classification result, obtain final classification result;Classification result post-processing.The application effectively improves the precision and regional integrity of topography and geomorphology classification by using TIN to construct local reference bottom surface, can realize the high-precision automatic division of global topography and geomorphology class in the case of only using DEM as original data, provides solid data foundation for global geomorphology research.

Description

Technical Field

[0001] This invention belongs to the field of automatic classification technology of natural landforms, specifically relating to an automatic classification method of landforms based on a local reference base, and more specifically to an automatic classification method of landforms based on an irregular triangular network. Background Technology

[0002] Landforms refer to the geometric features of the Earth's surface, characterized by its varying elevations and depressions, and are the result of the interaction of internal and external geological forces. The landforms of any geographical region on the Earth's surface are composed of repeated and alternating landform features. The rational classification of landform types is fundamental to the study of landform combinations.

[0003] Geomorphological classification is the division of Earth's surface morphological units according to their morphology, origin, material composition, age, and other attributes, based on their inherent logical relationships. Due to differences in classification principles and methods, various geomorphological classification systems have emerged. However, with the development of geomorphological classification theory, modern geomorphology has gradually established a dominant classification system centered on origin and morphology. Under this system, global landforms can be divided into three grades and six levels: geomorphic classes, geomorphic categories, and geomorphic types. These three grades are further subdivided into six types: geomorphic classes, geomorphic subclasses, geomorphic categories, geomorphic subclasses, geomorphic types, and geomorphic subtypes. The classification of geomorphic classes and subclasses is called basic geomorphological classification, also known as topographic geomorphological classification. The further subdivision of geomorphic categories and subclasses is called geomorphic origin classification. Geomorphic types are obtained by further subdividing the morphology of geomorphic categories. The classification of geomorphic subtypes involves geomorphic types related to material composition.

[0004] Topographic and geomorphological classification is based on the macroscopic morphological characteristics and altitude of landforms. The commonly used topographic and geomorphological classification method divides the land surface into seven specific geomorphic classes: plains, plateaus, hills, gently undulating mountains, moderately undulating mountains, heavily undulating mountains, and extremely undulating mountains. These geomorphic classes are further subdivided into four subclasses based on altitude: low altitude, medium altitude, high altitude, and extremely high altitude, thus forming 24 basic landform types.

[0005] In the early days, geomorphological classification and mapping relied primarily on manual visual interpretation of topographic maps and field surveys. This method was not only costly and inefficient, but also resulted in some areas being unclassifiable. With the development of geographic information science and technology, Digital Elevation Models (DEMs), due to their rich detail and rapid computation, have gradually become the data foundation for automatic geomorphological classification. This has led to the development of a series of automatic geomorphological classification methods that primarily use DEMs as their data source. Existing automatic geomorphological classification methods based on DEMs mainly include clustering-based methods, expert knowledge-based rule-based methods, and machine learning-based methods. Clustering-based classification methods are effective in extracting single landform types, but because they are data-driven, they lack clear geographical significance and are difficult to reflect the actual spatial distribution patterns of landforms. Expert knowledge-based classification methods transform expert knowledge into rules, which have clear geographical significance, but the rule-making process of expert knowledge is difficult, and the method has poor universality. Machine learning-based methods usually combine typical landform sample areas with machine learning methods to mine the implicit information in the sample areas. This method considers the spatial location relationship of the data and expert knowledge, but the classification results are limited by the landform richness in the selected sample areas.

[0006] The automatic topographic and geomorphological classification method proposed in this invention belongs to the category of expert knowledge-based classification methods. Based on Zhou Chenghu's 1:1,000,000 digital geomorphological classification system for China, it uses topographic relief and altitude as the main classification indicators to divide global landforms into 23 categories according to morphology (low-altitude plains, medium-altitude plains, high-altitude plains, extremely high-altitude plains, low-altitude hills, medium-altitude hills, high-altitude hills, extremely high-altitude hills, slightly undulating low mountains, slightly undulating medium mountains, slightly undulating high mountains, slightly undulating extremely high mountains, medium-undulating low mountains, medium-undulating medium mountains, medium-undulating high mountains, medium-undulating extremely high mountains, highly undulating low mountains, highly undulating medium mountains, highly undulating high mountains, highly undulating extremely high mountains, extremely undulating medium mountains, extremely undulating high mountains, extremely undulating medium mountains, extremely undulating high mountains, extremely undulating extremely high mountains). Unlike automatic topographic and geomorphological classification methods that use pixels as the basic unit, this invention incorporates consideration of the overall macroscopic topographic and geomorphological structure, improving the consistency of the classification results. Summary of the Invention

[0007] Purpose of the invention: Considering the limitations of traditional automatic topographic and geomorphological classification methods, this invention proposes an automatic topographic and geomorphological classification method based on a local reference surface. It uses only DEM as the data source and incorporates the concept of a local reference surface, making it a global automatic topographic and geomorphological classification method that can simultaneously take into account the integrity of the landform and the accuracy of classification.

[0008] Technical solution: An automatic topographic and geomorphological classification method based on a local reference base, the steps of which are as follows:

[0009] (1) Generate the cumulative runoff data of the study area based on the original DEM data of the study area;

[0010] (2) Based on the cumulative flow data, the flow threshold is adaptively determined to construct the river network. The elevation control points of the local reference bottom surface of the study area are generated from the river network, and then an irregular triangular network is constructed. The irregular triangular network is converted into a raster to generate the local reference bottom surface DEM. The local reference bottom surface proposed by this method is the base surface of the local terrain simulated by the relatively low ground points in the study area, which is the reference for the calculation of the undulation of the study area.

[0011] (3) Subtract the local baseline DEM from the original DEM data to obtain the surface relief of the study area. Set a certain relief classification threshold to divide the plains and mountains. For the mountainous part, perform secondary classification based on the relief magnitude.

[0012] (4) Using elevation as the classification standard, the original DEM of the study area is classified into global elevations to obtain the automatic topographic and geomorphological classification results that combine elevation and relief.

[0013] (5) Perform fine patch elimination and misclassified patch correction on the results of step (4), smooth unreasonable type boundaries, and obtain the final topographic and geomorphological classification results.

[0014] Preferably, the specific steps for generating the cumulative runoff data of the study area based on the original DEM data of the study area in step (1) are as follows:

[0015] (101) Based on the scope of the study area, the DEM data within the area is cropped and stitched together, and only the land portion of the study area is retained to obtain the original DEM data of the study area;

[0016] (102) Based on the original DEM data of the study area, the surface flow direction data of the study area is calculated based on the priority flooding algorithm;

[0017] (103) Based on the original DEM data of the study area and the calculated surface flow direction data, calculate the cumulative runoff data of the study area.

[0018] Preferably, the steps for generating the local reference bottom surface DEM in step (2) are as follows:

[0019] (201) Based on the cumulative runoff data generated in step (1), an adaptive runoff threshold is generated according to the original DEM data of the study area. Runoff rasters with values ​​greater than the threshold are extracted as river networks, and rasters with values ​​less than the threshold are set to null values.

[0020] (202) Based on the river network data generated in step (201) as the control points of the baseline, extract the elevation values ​​of the corresponding locations in the original DEM data to the river network grid.

[0021] (203) The local reference bottom surface control points generated in step (202) are converted from grid into discrete vector points, which are used as elevation control points for constructing the local reference bottom surface;

[0022] (204) Using the local reference bottom elevation control points generated in step (203) as input, construct a TIN that conforms to Delaunay triangulation, i.e., the local reference bottom of the study area;

[0023] (205) Based on the TIN generated in step (204), convert the TIN into a local reference bottom surface DEM with the same grid size and range as the original DEM.

[0024] Preferably, the GIS software used in step (3) includes ArcGIS Pro.

[0025] Preferably, the undulation classification threshold in step (3) is 30.

[0026] As a preferred embodiment, the secondary classification method for plains and mountains, as well as the mountainous portion, in step (3) is as follows:

[0027] (301) Subtract the original DEM data from the local reference bottom surface DEM data to obtain the undulation grid of the study area;

[0028] (302) Classify the results generated in step (301) according to the undulation classification threshold of 30 meters. Grids with a value greater than the threshold are classified as mountains, and those with a value less than the undulation threshold are classified as plains. Generate mountain and plain category result data containing only 0 and 1, where 0 represents mountains and 1 represents plains.

[0029] (303) Based on the mountain and plain range generated in step (302), the two categories of mountains and plains are separated into two independent raster data;

[0030] (304) Based on the mountain grid generated in step (303), take the union of it with the undulation grid generated in (301), and further classify the mountains according to the undulation classification criteria. The undulation classification criteria are 200, 500, 1000 and 2500, which respectively divide the mountains in the study area into five categories: hills, small undulation mountains, medium undulation mountains, large undulation mountains and extremely undulation mountains.

[0031] Preferably, the specific steps for obtaining the integrated elevation classification result and relief classification result in step (4) to obtain the automatic topographic classification result are as follows:

[0032] (401) Classify the original DEM data of the study area according to the elevation classification standard. Based on the three classification thresholds of 1000, 3500 and 5000, the study area is divided into four categories: low altitude, medium altitude, high altitude and extremely high altitude, and an elevation classification result raster is generated.

[0033] (402) Based on the plain raster data generated in step (303), the plain raster is obtained by taking the union of the plain raster and the elevation classification result in (401) using a raster calculator. The generated plain elevation classification result is only the unit digit representing the elevation in the classification code. According to the classification coding standard, the plain elevation classification result is added with the value 100 representing the plain category to obtain the plain classification result raster. The specific formula is as follows:

[0034] ("%Elevation Classification Results%"*"%Plain Raster%")+100;

[0035] (403) Based on the mountain raster data generated in step (303), multiply it with the elevation classification result to obtain the elevation classification data of the mountain range. According to the classification coding standard, multiply the code corresponding to the mountain relief classification result generated in step (304) by 10, add the elevation classification result of the mountain range and the value 200 representing the mountain category to obtain the mountain classification result code. The specific formula is as follows:

[0036] ("%Elevation Classification Results%"*"%Mountain Raster%")+(%Mountain Relief Classification%*10)+200;

[0037] (404) Based on the plain classification result raster generated in (402) and the mountain classification result raster generated in (403), the topographic and geomorphological classification results of the study area are merged. The specific coding adopts a three-digit decimal category code, the meaning of which is as follows: the hundreds digit represents whether the area belongs to plain or mountain, with the code for plain being 1 and the code for mountain being 2; the tens digit represents further subdivision within the mountains and plains, with 0 for plain areas; in mountain areas, 1 represents hills, 2 represents slightly undulating mountains, 3 represents moderately undulating mountains, 4 represents highly undulating mountains, and 5 represents extremely undulating mountains; the units digit represents the elevation classification result, with 1 representing low altitude, 2 representing medium altitude, 3 representing high altitude, and 4 representing extremely high altitude.

[0038] Preferably, in step (5), the results of step (4) are processed to eliminate fragmented patches and correct misclassified patches, and unreasonable type boundaries are smoothed. The specific steps are as follows:

[0039] Step (501) is based on the classification results generated in step (404), and converts the generated raster classification results into vectors while retaining the classification codes in the raster data;

[0040] Step (502) Based on the vector classification results generated in step (501), unreasonable fragmented patches in the classification results are eliminated according to a threshold of 0.16 square kilometers, and then the elimination results are smoothed by a shared boundary with a tolerance of 7 grids.

[0041] Preferably, in step (2), the adaptive determination of the confluence threshold is specifically the ratio of the actual number of rasters in the original DEM data of the study area to the number of rasters in the standard map sheet DEM multiplied by the predefined standard map sheet confluence accumulation amount, where the predefined standard map sheet confluence accumulation amount is the optimal confluence accumulation amount for that standard map sheet.

[0042] Beneficial effects:

[0043] 1. This invention constructs a local baseline that approximates the actual topographic relief by using river networks and triangular irregular networks (TINs), effectively solving the problem of calculating topographic relief in classification indicators. Compared with traditional automatic topographic classification methods, it greatly reduces the fragmentation of classification results and significantly improves the regional consistency of classification results, making the classification results closer to the real topographic distribution on the Earth's surface.

[0044] 2. The method proposed in this invention realizes fully automatic topographic and geomorphological classification using only DEM as raw data. It achieves fast and high-precision automatic classification of global topographic and geomorphological features without relying on other data sources, thus accelerating the global geomorphological classification process, providing a solid data foundation for global geomorphological research, and also providing a new approach to realizing automatic global geomorphological classification.

[0045] 3. This invention uses an adaptive threshold to reduce interference from human factors, reduce unreasonable leaps between different categories, and improve the smoothness of category transitions.

[0046] In summary, by using TIN, this invention constructs a local baseline with the river network as the basic framework, making the calculation of topographic relief more reasonable and accurate, the classification results more precise, and the results more complete. Secondly, the geomorphological classification results constructed with the river network as the baseline are more in line with the natural laws of geomorphological formation and have stronger semantic information. Attached Figure Description

[0047] Figure 1 This is a flowchart of the automatic topographic and geomorphological classification method based on a local reference base surface according to the present invention;

[0048] Figure 2 This is a DEM data map of the experimental sample area in Embodiment 2 of the present invention;

[0049] Figure 3 This is the sample area flow diagram in Embodiment 2 of the present invention;

[0050] Figure 4 This is a cumulative flow diagram of the sample area in Embodiment 2 of the present invention;

[0051] Figure 5 This is a vector point map of the river network in the sample area in Embodiment 2 of the present invention;

[0052] Figure 6 This is the TIN diagram of the local reference bottom surface of the sample area in Embodiment 2 of the present invention;

[0053] Figure 7 This is a local reference bottom surface grid diagram of the sample area in Embodiment 2 of the present invention;

[0054] Figure 8 This is the topographic and geomorphological classification and coding map of the present invention;

[0055] Figure 9 This is a map showing the mountainous and plain area of ​​the sample area in Embodiment 2 of the present invention;

[0056] Figure 10 This is a topographic and geomorphological classification result map of the sample area in Embodiment 2 of the present invention. Detailed Implementation

[0057] The present invention will be further described in detail below with reference to the accompanying drawings. The scope of protection of the present invention is not limited to the embodiments described.

[0058] This invention proposes an automatic topographic and geomorphological classification method based on a local reference base, such as... Figure 1 As shown, the specific steps include:

[0059] (1) Generate the cumulative runoff data of the study area based on the original DEM of the study area;

[0060] (2) Based on the cumulative flow data, the flow threshold is adaptively determined to construct the river network. The elevation control points of the local reference bottom surface of the study area are generated from the river network, and then an irregular triangular network is constructed. The irregular triangular network is converted into a raster to generate the local reference bottom surface DEM.

[0061] (3) Subtract the local baseline DEM from the original DEM data to obtain the surface relief of the study area. Set a certain relief classification threshold to divide the plains and mountains. For the mountainous part, perform secondary classification based on the relief magnitude.

[0062] (4) Using elevation as the classification standard, the original DEM of the study area is classified into global elevations. The elevation classification results and relief classification results are combined to obtain the automatic topographic and geomorphological classification results.

[0063] (5) Perform fine patch elimination and misclassified patch correction on the results of step (4), smooth unreasonable type boundaries, and obtain the final topographic and geomorphological classification results.

[0064] Example 1

[0065] In this example, an automatic terrain classification method based on a local reference bottom surface is as follows:

[0066] Step (1): Generate the flow accumulation data of the study area based on the original DEM data of the study area. The specific steps are as follows:

[0067] (101) According to the scope of the study area, crop and splice the DEM data within the area, and only retain the land part of the study area to obtain the original DEM data of the study area;

[0068] (102) Based on the original DEM data of the study area, calculate the surface flow direction data within the study area based on the priority flooding algorithm;

[0069] (103) Based on the original DEM data of the study area and the calculated surface flow direction data, calculate the flow accumulation data of the study area.

[0070] Step (2): Based on the flow accumulation data, adaptively determine the flow threshold to construct a river network, generate elevation control points of the local reference bottom surface of the study area from the river network, and then construct an irregular triangular network. Convert the irregular triangular network into a raster to generate the local reference bottom surface DEM. The adaptive determination of the flow threshold is specifically the ratio of the actual raster number of the original DEM data of the study area to the raster number of the standard map sheet DEM multiplied by the predefined flow accumulation of the standard map sheet. The predefined flow accumulation of the standard map sheet is the optimal flow accumulation of the standard map sheet.

[0071] The generation steps of the local reference bottom surface DEM are specifically as follows:

[0072] (201) Based on the flow accumulation data generated in step (1), generate an adaptive flow threshold according to the ratio of the actual raster number of the original DEM data of the study area to the raster number of the standard map sheet DEM multiplied by the predefined flow accumulation of the standard map sheet. The flow raster greater than the threshold is extracted as the river network, and the raster less than the threshold is set to a null value;

[0073] (202) Based on the river network data and the original DEM data generated in step (201), use the SetNull function in the raster calculator to set the raster less than the flow threshold to a null value, and set the raster greater than or equal to the flow threshold to the elevation value of the original DEM at the corresponding position. The specific formula of the SetNull function is as follows:

[0074] SetNull("% flow accumulation %"<float("% flow threshold %"),"% original DEM %");

[0075] (203) The local reference bottom surface control points generated in step (202) are converted from grid into discrete vector points, which are used as elevation control points for constructing the local reference bottom surface;

[0076] (204) Using the local reference bottom elevation control points generated in step (203) as input, construct a TIN that conforms to Delaunay triangulation, i.e., the local reference bottom of the study area;

[0077] (205) Based on the TIN generated in step (204), convert the TIN into a local reference bottom surface DEM with the same grid size and range as the original DEM.

[0078] Step (3): Subtract the local baseline DEM from the original DEM data to obtain the surface relief of the study area. Set a certain relief classification threshold to divide the plains and mountains. For the mountainous part, perform secondary classification based on the relief size. The GIS software used includes ArcGIS Pro.

[0079] The specific secondary classification method for plains, mountains, and mountainous areas is as follows:

[0080] (301) Subtract the original DEM data from the local reference bottom surface DEM data generated in step (2) to obtain the undulation grid of the study area;

[0081] (302) Classify the results generated in step (301) according to the undulation classification threshold of 30 meters. Grids with a value greater than the threshold are classified as mountains, and those with a value less than the undulation threshold are classified as plains. Generate mountain and plain category result data containing only 0 and 1, where 0 represents mountains and 1 represents plains.

[0082] (303) Based on the mountain and plain range generated in step (302), the two categories of mountains and plains are separated into two independent raster data;

[0083] (304) Based on the mountain grid generated in step (303), take the union of it with the undulation grid generated in (301), and further classify the mountains according to the undulation classification criteria. The undulation classification criteria are 200, 500, 1000 and 2500, which respectively divide the mountains in the study area into five categories: hills, small undulation mountains, medium undulation mountains, large undulation mountains and extremely undulation mountains.

[0084] Step (4): Using elevation as the classification standard, perform global elevation classification on the original DEM of the study area, and combine the elevation classification results and the relief classification results to obtain the automatic topographic and geomorphological classification results.

[0085] By combining the elevation classification results and the relief classification results, the automatic topographic and geomorphological classification results are obtained.

[0086] The steps are as follows:

[0087] (401) Classify the original DEM data of the study area according to the elevation classification standard. Based on the three classification thresholds of 1000, 3500 and 5000, the study area is divided into four categories: low altitude, medium altitude, high altitude and extremely high altitude, and an elevation classification result raster is generated.

[0088] (402) Based on the plain raster data generated in step (303), the plain raster is obtained by taking the union of the plain raster and the elevation classification result in (401) using a raster calculator. The generated plain elevation classification result is only the unit digit representing the elevation in the classification code. According to the classification coding standard, the plain elevation classification result is added with the value 100 representing the plain category to obtain the plain classification result raster. The specific formula is as follows:

[0089] ("%Elevation Classification Results%"*"%Plain Raster%")+100;

[0090] (403) Based on the mountain raster data generated in step (303), multiply it with the elevation classification result to obtain the elevation classification data of the mountain range. According to the classification coding standard, multiply the code corresponding to the mountain relief classification result generated in step (304) by 10, add the elevation classification result of the mountain range and the value 200 representing the mountain category to obtain the mountain classification result code. The specific formula is as follows:

[0091] ("%Elevation Classification Results%"*"%Mountain Raster%")+(%Mountain Relief Classification%*10)+200;

[0092] (404) Based on the plain classification result raster generated in (402) and the mountain classification result raster generated in (403), the topographic and geomorphological classification results of the study area are merged. The specific coding adopts a three-digit decimal category code, the meaning of which is as follows: the hundreds digit represents whether the area belongs to plain or mountain, with the code for plain being 1 and the code for mountain being 2; the tens digit represents further subdivision within mountains and plains, with 0 for plains; in mountainous areas, 1 represents hills, 2 represents slightly undulating mountains, 3 represents moderately undulating mountains, 4 represents highly undulating mountains, and 5 represents extremely undulating mountains; the units digit represents the elevation classification result, with 1 representing low altitude, 2 representing medium altitude, 3 representing high altitude, and 4 representing extremely high altitude.

[0093] (5) Perform fine patch elimination and misclassified patch correction on the results of step (4), and smooth unreasonable type boundaries. The specific steps are as follows:

[0094] Step (501) is based on the classification results generated in step (404), and converts the generated raster classification results into vectors while retaining the classification codes in the raster data;

[0095] Step (502) Based on the vector classification results generated in step (501), unreasonable fragmented patches in the classification results are eliminated according to a threshold of 0.16 square kilometers. Then, the elimination results are smoothed by a shared boundary with a tolerance of 7 grids to obtain the final terrain and landform classification results.

[0096] Example 2

[0097] This embodiment presents an automatic topographic and geomorphological classification method based on a local reference base, the steps of which are as follows:

[0098] Step 1: Generate the cumulative runoff data of the study area based on the original DEM data of the study area.

[0099] DEM (Digital Elevation Model) is a digital representation of a terrain surface. Because it contains rich topographic and geomorphological information, it serves as a carrier of various geographic information and is one of the fundamental data sets for national geographic information. The experimental sample area used in this embodiment is located in the Namcha Barwa peak area on the southern slope of the Himalayas. The sample area range is as follows: Figure 2 As shown. The experimental sample area data is FAB DEM (Forest And Buildings removed Copernicus DEM) with a spatial resolution of 1 arcsecond. The gray value of a point in the image represents the elevation value of the corresponding location on the ground surface. As the gray value increases, the elevation value also increases, and the pixels in the image gradually become brighter.

[0100] The specific steps for generating cumulative runoff data for the study area based on the original DEM data of the study area are as follows:

[0101] 1) Based on the scope of the study area, the DEM data within the area is cropped and stitched together, retaining only the land portion of the study area to obtain the original DEM data of the study area;

[0102] 2) Using the original DEM data of the study area as input, the surface flow direction data of the study area are calculated based on the priority flooding algorithm;

[0103] 3) The generated flow direction data does not contain any coordinate system. Define geographic coordinates for the flow direction data in ArcGIS Pro, and the result will be as follows: Figure 3 As shown, the geographic coordinate system in this embodiment is WGS84;

[0104] 4) Based on flow direction data and the original DEM data of the study area, the cumulative runoff volume of the study area is calculated, and the results are as follows: Figure 4 As shown.

[0105] Step 2: Based on the flow accumulation data, adaptively determine the flow threshold to construct the river network, generate elevation control points of the local reference bottom surface of the study area from the river network, and then construct a Triangulated Irregular Network (TIN). Convert the TIN into a raster to generate the local reference bottom surface DEM. The specific method for adaptively determining the flow threshold is to multiply the ratio of the actual raster number of the original DEM data in the study area to the raster number of the standard map DEM by the predefined standard map flow accumulation. The predefined standard map flow accumulation is the optimal flow accumulation of the standard map.

[0106] A Triangulated Irregular Network (TIN) is a data structure used to describe surface models in geographic information systems. This structure is composed of non - intersecting and non - overlapping triangles, which can reflect the detailed features of terrain and landforms more accurately than regular grids. The original data of the TIN usually comes from the terrain feature points measured in the field within the survey area.

[0107] The steps for generating the local reference bottom surface DEM are as follows:

[0108] 1) Based on the surface flow accumulation data, generate an adaptive flow threshold by multiplying the ratio of the actual raster number of the original DEM data in the study area to the raster number of the standard map DEM by the predefined standard map flow accumulation. The flow raster greater than this threshold is extracted as the river network, and the raster less than the threshold is set to null. In this example, the 10°×10° raster in the Qinghai - Tibet Plateau region is used as the standard map range, and the optimal flow accumulation of this map is used as the standard flow accumulation.

[0109] 2) According to the determined flow threshold, set the raster less than the flow threshold to null, and set the raster greater than or equal to the flow threshold to the elevation value of the original DEM at the corresponding position. The generated river network is as Figure 5 shown, and in this example, it is implemented through the SetNull function in the raster calculator. The specific formula is as follows:

[0110] SetNull("%Flow accumulation%"<float("%Flow threshold%"),"%Original DEM%")

[0111] 3) Through raster to point conversion, convert the river network raster data extracted in 2) into river network vector point data. The assignment field of the output vector data is Value. At this time, the Value field of the raster data contains the elevation value of the DEM, so the generated river network vector points carry the DEM elevation value, that is, the elevation control points of the local reference bottom surface.

[0112] 4) Using the elevation control points of the local reference bottom surface as the input, construct a TIN that conforms to Delaunay triangulation, as Figure 6 shown;

[0113] 5) Obtain the original DEM raster cell size, and use it as a reference to convert the TIN into a local reference bottom surface DEM with the same raster size as the original DEM, such as... Figure 7 As shown.

[0114] Step 3: Subtract the local baseline DEM from the original DEM data to obtain the surface relief of the study area. Set a certain relief threshold to divide the area into plains and mountains. For the mountainous areas, perform secondary classification based on the relief magnitude. The GIS software used includes ArcGIS Pro.

[0115] The specific secondary classification method for plains, mountains, and mountainous areas is as follows:

[0116] 1) Subtract the original DEM data of the study area from the local reference bottom surface DEM data generated in step 2 to obtain the undulation raster of the study area;

[0117] 2) In most cases, a relief threshold of 30 meters can effectively distinguish between plains and mountains. Therefore, this embodiment selects 30 meters as the relief classification threshold. Raster cells with a relief greater than the threshold are classified as mountains, and those with a relief less than the threshold are classified as plains. This generates raster data containing only 0 and 1 for mountain and plain classification results, where 0 represents mountains and 1 represents plains. The classification results are as follows: Figure 9 As shown.

[0118] 3) Based on the mountain and plain range generated in 2), the two categories of mountains and plains are separated into two independent raster data;

[0119] 4) Based on the mountain raster generated in 3), take its union with the relief raster generated in 1). According to the relief classification criteria, further classify the mountains. The relief classification criteria are 200, 500, 1000, and 2500. The mountains in the study area are divided into five categories: hills, slightly undulating mountains, moderately undulating mountains, heavily undulating mountains, and extremely undulating mountains. Generate mountain relief codes: less than 200 is hills, coded as 1; greater than or equal to 200 and less than 500 is slightly undulating mountains, coded as 2; greater than or equal to 500 and less than 1000 is moderately undulating mountains, coded as 3; greater than or equal to 1000 and less than 2500 is heavily undulating mountains, coded as 4; and greater than or equal to 2500 is extremely undulating mountains, coded as 5. The resulting mountain relief classification raster is obtained.

[0120] Step 4: Perform global elevation classification on the original DEM of the study area using elevation as the classification standard to obtain comprehensive elevation classification results and relief classification results, thus obtaining automatic topographic and geomorphological classification results. The specific steps are as follows:

[0121] 1) The original DEM data of the study area was classified according to the elevation classification standard. Based on the three classification thresholds of 1000, 3500 and 5000, the study area was divided into four categories: low altitude, medium altitude, high altitude and extremely high altitude. Among them, less than 1000 is low altitude and is coded as 1; greater than or equal to 1000 and less than 3500 is medium altitude and is coded as 2; greater than or equal to 3500 and less than 5000 is high altitude and is coded as 3; and greater than or equal to 5000 is extremely high altitude and is coded as 4. The elevation classification result raster was generated.

[0122] 2) Based on the generated plain raster data, the plain raster and elevation classification results are combined using a raster calculator to obtain the plain elevation classification result. The generated plain elevation classification result only contains the units digit representing the elevation in the classification code. According to the classification coding standard, the plain elevation classification result is supplemented with the value 100 representing the plain category to obtain the plain classification result raster. The specific formula is as follows:

[0123] ("%Elevation Classification Results%"*"%Plain Raster%")+100

[0124] 3) Based on the generated mountain raster data, multiply it with the elevation classification result to obtain the elevation classification data of the mountain area. According to the classification coding standard, multiply the code corresponding to the mountain relief classification result generated in step 3 by 10, add the elevation classification result of the mountain area and the value 200 representing the mountain category, and obtain the mountain classification result code. The specific formula is as follows:

[0125] ("%Elevation Classification Results%"*"%Mountain Raster%")+(%Mountain Relief Classification%*10)+200

[0126] 4) Combine the plain classification results raster generated in 2) and the mountain classification results raster generated in 3) to obtain the topographic and geomorphological classification results of the study area, specifically coded as follows: Low-altitude plain 101, Mid-altitude plain 102, High-altitude plain 103, Extremely high-altitude plain 104, Low-altitude hills 211, Mid-altitude hills 212, High-altitude hills 213, Extremely high-altitude hills 214, Slightly undulating low mountains 221, Slightly undulating medium mountains 222, Slightly undulating high mountains 223, Slightly undulating extremely high mountains 224, Medium-undulating low mountains 231, Medium-undulating medium mountains 232, Medium-undulating high mountains 233, Medium-undulating extremely high mountains 234, Largely undulating low mountains 241, Largely undulating medium mountains 242, Largely undulating low mountains 243, Largely undulating medium mountains 244, Largely undulating low mountains 245, Largely undulating medium mountains 246, Largely undulating low mountains 247, Largely undulating medium mountains 248, Largely undulating low mountains 249, Largely undulating low mountains 240, Largely undulating medium mountains 241, Largely undulating low mountains 242, Largely undulating low mountains 243, Largely undulating low mountains 244, Largely undulating low mountains 245, Largely undulating low mountains 246, Largely undulating low mountains 247, Largely undulating low mountains 248, Largely undulating low mountains 249, Largely undulating low mountains 240, Largely undulating low mountains 241, Largely undulating medium mountains 242, Largely undulating low mountains 243, There are 23 categories in total: undulating high mountains (243), highly undulating extremely high mountains (244), moderately undulating medium mountains (252), highly undulating high mountains (253), and highly undulating extremely high mountains (254). The three-digit decimal category codes represent the following: the hundreds digit indicates whether the area is a plain or a mountain (1 for plains, 2 for mountains); the tens digit represents further subdivision within mountains and plains (0 for plains); in mountainous areas, 1 represents hills, 2 represents slightly undulating mountains, 3 represents moderately undulating mountains, 4 represents highly undulating mountains, and 5 represents extremely undulating mountains; the units digit represents the elevation classification result (1 for low altitude, 2 for medium altitude, 3 for high altitude, 4 for extremely high altitude). The classification table is as follows: Figure 8 As shown.

[0127] Step 5: Perform fragmented patch elimination and misclassified patch correction on the results of Step 4, smooth unreasonable type boundaries, and obtain the final terrain and geomorphology classification results. The specific steps are as follows:

[0128] 1) Based on the classification results generated in step 4, convert the generated raster classification results into vector surface data, while retaining the classification codes in the raster data;

[0129] 2) For the generated vector classification results, unreasonable fragmented patches in the results are eliminated according to the debris elimination threshold. In this example, the debris elimination threshold is 0.16 square kilometers.

[0130] 3) Next, smooth the boundaries of the vector results according to the elimination results using a smoothing threshold. In this example, the smoothing threshold is set to 210 meters, which is the size of 7 raster cells, to obtain the final terrain and landform classification results. The generated basic classification results are as follows: Figure 10 As shown.

Claims

1. An automatic topographic and geomorphological classification method based on a local reference base, characterized in that, The steps are as follows: (1) Generate the cumulative runoff data of the study area based on the original DEM data of the study area; (2) Based on the cumulative flow data, the flow threshold is adaptively determined to construct the river network. The elevation control points of the local reference bottom surface of the study area are generated from the river network, and then an irregular triangular network is constructed. The irregular triangular network is converted into a raster to generate the local reference bottom surface DEM. (3) Subtract the local baseline DEM from the original DEM data to obtain the surface relief of the study area. Set a certain relief classification threshold to divide the plains and mountains. For the mountainous part, perform secondary classification based on the relief magnitude, as follows: (301) Calculate the difference between the original DEM and the local reference bottom surface DEM to obtain the undulation grid of the study area; (302) Classify the results generated in step (301) according to the undulation classification threshold of 30 meters. Grids with a value greater than the threshold are classified as mountains, and those with a value less than the undulation threshold are classified as plains. Generate mountain and plain category result data containing only 0 and 1, where 0 represents mountains and 1 represents plains. (303) Based on the mountain and plain range generated in step (302), separate the two categories of mountains and plains into two independent raster data; (304) Based on the mountain grid generated in step (303), take the union of it with the undulation grid generated in step (301), and further classify the mountains according to the undulation classification criteria. The undulation classification criteria are 200, 500, 1000 and 2500, which respectively divide the mountains in the study area into five categories: hills, small undulation mountains, medium undulation mountains, large undulation mountains and extremely undulation mountains. (4) Using elevation as the classification standard, the original DEM of the study area is classified into global elevations. The results of elevation classification and relief classification are combined to obtain the automatic topographic and geomorphological classification results, as follows: (401) Classify the original DEM data of the study area according to the elevation classification standard. Based on the three classification thresholds of 1000, 3500 and 5000, the study area is divided into four categories: low altitude, medium altitude, high altitude and extremely high altitude, and an elevation classification result raster is generated. (402) Based on the plain raster data generated in step (303), the plain raster is obtained by taking the union of the plain raster and the elevation classification result in (401) using a raster calculator. The generated plain elevation classification result is only the unit digit representing the elevation in the classification code. According to the classification coding standard, the plain elevation classification result is added to the value 100 representing the plain category to obtain the plain classification result raster. The specific formula is as follows: ； (403) Based on the mountain raster data generated in step (303), multiply it with the elevation classification result to obtain the elevation classification data of the mountain range. According to the classification coding standard, multiply the code corresponding to the mountain relief classification result generated in step (304) by 10, add the elevation classification result of the mountain range and the value 200 representing the mountain category to obtain the mountain classification result code. The specific formula is as follows: ； (404) Based on the plain classification result raster generated in (402) and the mountain classification result raster generated in (403), the topographic and geomorphological classification results of the study area are merged. The specific coding adopts a three-digit decimal category code, the meaning of which is as follows: the hundreds digit represents whether the area belongs to plain or mountain, with plain code 1 and mountain code 2; the tens digit represents further subdivision within mountains and plains, with all plains having a code of 0; in mountainous areas, 1 represents hills, 2 represents slightly undulating mountains, 3 represents moderately undulating mountains, 4 represents highly undulating mountains, and 5 represents extremely undulating mountains; the units digit represents the elevation classification result, with 1 representing low altitude, 2 representing medium altitude, 3 representing high altitude, and 4 representing extremely high altitude. (5) Perform fragmented patch elimination and misclassified patch correction on the results of step (4), smooth unreasonable type boundaries, and obtain the final topographic and geomorphological classification results.

2. The automatic topographic and geomorphological classification method based on a local reference base as described in claim 1, characterized in that, The specific steps for generating the cumulative runoff data of the study area based on the original DEM data of the study area in step (1) are as follows: (101) Based on the scope of the study area, the DEM data within the scope is cropped and stitched together, retaining only the land portion of the study area to obtain the original DEM data of the study area; (102) Based on the original DEM data of the study area, the surface flow direction data of the study area are calculated based on the priority flooding algorithm; (103) Based on the original DEM data of the study area and the calculated surface flow direction data, calculate the cumulative runoff data of the study area.

3. The automatic topographic and geomorphological classification method based on a local reference base as described in claim 1, characterized in that, The specific steps for generating the local reference bottom surface DEM in step (2) are as follows: (201) Based on the cumulative flow data generated in step (1), an adaptive flow threshold is generated according to the original DEM data of the study area. Flow grids with values ​​greater than the threshold are extracted as river networks, and grids with values ​​less than the threshold are set to null values. (202) Using the river network data generated in step (201) as the control points of the baseline, extract the elevation values ​​of the corresponding locations in the original DEM data to the river network grid; (203) The local reference bottom surface control points generated in step (202) are converted from grid into discrete vector points, which are used as elevation control points for constructing the local reference bottom surface; (204) Using the local reference bottom elevation control points generated in step (203) as input, construct a TIN that conforms to Delaunay triangulation, i.e., the local reference bottom of the study area; (205) Based on the TIN generated in step (204), convert the TIN into a local reference bottom surface DEM with the same grid size and range as the original DEM.

4. The automatic topographic and geomorphological classification method based on a local reference base as described in claim 1, characterized in that, The GIS software used in step (3) includes ArcGIS Pro.

5. The automatic topographic and geomorphological classification method based on a local reference base as described in claim 1, characterized in that, The undulation classification threshold in step (3) is 30.

6. The automatic topographic and geomorphological classification method based on a local reference base as described in claim 1, characterized in that, In step (5), the results of step (4) are processed to eliminate fragmented patches and correct misclassified patches, and unreasonable type boundaries are smoothed. The specific steps are as follows: Step (501) is based on the classification results generated in step (404), and converts the generated raster classification results into vectors while retaining the classification codes in the raster data; Step (502) Based on the vector classification results generated in step (501), unreasonable fragmented patches in the classification results are eliminated according to a threshold of 0.16 square kilometers, and then the elimination results are smoothed by a shared boundary with a tolerance of 7 grids.

7. The automatic topographic and geomorphological classification method based on a local reference base as described in claim 1, characterized in that, In step (2), the adaptive determination of the confluence threshold is specifically the ratio of the actual number of rasters in the original DEM data of the study area to the number of rasters in the standard map sheet DEM, multiplied by the predefined standard map sheet confluence accumulation amount. The predefined standard map sheet confluence accumulation amount is the optimal confluence accumulation amount for that standard map sheet.

Images