Bank collapse range prediction method based on land and water integrated elevation angle

Abstract

The invention discloses a bank collapse range prediction method based on a land and water integrated elevation angle. The method comprises the following steps: 1, constructing an underwater and land integrated digital elevation model (DEM) of a research area; 2, constructing a translation model of the DEM based on matrix convolution operation, and calculating a three-dimensional lowest elevation angle (3D LEA) from the DEM based on the translation model; 3, cutting the 3D LEA according to a part above 0 m line to obtain 3D LEA distribution of a land area part, and taking the 3D LEA distribution as a bank collapse risk quantitative index; and 4, setting a repose angle ST of the underwater bank slope, and predicting a bank collapse range according to the relative size of the 3D LEA and the ST. According to the method, the underwater landform and land construction planning are combined, and a basis is provided for determining the near-water distance of shoreline land utilization.

Description

Technical Field

[0001] The invention belongs to the field of GIS image processing, and in particular relates to a method for predicting the range of bank collapse based on the integrated water-land elevation angle. Background Art

[0002] With the intensification of global climate change and human activities, bank collapse, as a natural disaster, has posed a huge threat to coastal, riverside and lakeside areas. Bank collapse refers to the phenomenon that loose materials such as soil, sand and gravel near the coastline will slide and collapse under the action of gravity during the retreat of the coastline, riverside and lakeside. Bank collapse will not only lead to the retreat of the coastline, but also have a serious impact on the ecological environment, economic development and human life of coastal areas and the Yangtze River Economic Zone. Therefore, the prediction of the scope of bank collapse has become an urgent problem to be solved.

[0003] The prediction of the scope of bank collapse has a far-reaching impact on the economic development of coastal areas and the middle and lower reaches of the Yangtze River. The coastal area is one of the most economically developed regions in my country, and many important industries such as fisheries and tourism are developed here. The occurrence of bank collapse will lead to the retreat of the coastline, the reduction of land resources, and the damage of fishery resources and tourism resources. The bank collapse in the middle and lower reaches of the Yangtze River directly endangers flood control safety. By predicting the scope of bank collapse, we can rationally plan the economic development of coastal areas and the nearshore of the Yangtze River, avoid economic construction in high-risk areas of bank collapse, and reduce economic losses.

[0004] In addition, the prediction of the scope of bank collapse is of great significance to human life in coastal areas and the Yangtze River. Coastal areas and the Yangtze River are densely populated areas in my country, and many cities, villages and infrastructure are built here. The occurrence of bank collapse will have a serious impact on human life in coastal areas, such as house collapse and road interruption. By predicting the scope of bank collapse, urban and rural construction planning can be carried out in a targeted manner in coastal areas and the Yangtze River, avoiding infrastructure construction in high-risk areas of bank collapse and ensuring the safety of human life.

[0005] However, it is not easy to predict the scope of bank collapse. The occurrence of bank collapse is affected by many factors, such as topographic conditions, geological conditions, climate conditions, human activities, etc. The present invention uses underwater topography as the basis for predicting the scope of bank collapse on land, and proposes a method for predicting the scope of bank collapse based on the integrated water-land elevation angle. Summary of the invention

[0006] Purpose of the invention: The purpose of the present invention is to provide a method for predicting the range of bank collapse based on the integrated water-land elevation angle. By calculating the elevation angle and comparing it with the underwater critical slope, a rapid estimation of the range of bank collapse is achieved.

[0007] Technical solution: A method for estimating the range of bank collapse based on the integrated water-land elevation angle, comprising the following steps:

[0008] Step 1: Construct an integrated underwater and land digital elevation model (DEM) of the study area;

[0009] Step 2: Based on the matrix convolution operation, a translation model of the DEM is constructed, and the three-dimensional minimum elevation angle (3D LEA) is calculated from the DEM based on the translation model;

[0010] Step 3: Construct the shallow area below 0 m, perform mask analysis on the 3D LEA, and obtain the 3D LEA grid data of the land part as a quantitative indicator of bank collapse risk;

[0011] Step 4: Set the angle of repose ST of the underwater slope as the threshold, reclassify the 3D LEA, and use the high-value area as the prediction range of bank collapse.

[0012] Furthermore, the specific steps of step 1 are:

[0013] (11) Prepare underwater topographic data of the study area:

[0014] (a) According to the latitude and longitude range of the study area, a spatial coordinate system is established in the geographic information software, and the base map data is aligned with the coordinate system in combination with the latitude and longitude information of the nautical chart to obtain the geographic coordinate position data;

[0015] (b) digitize the shoreline, depth contours and water depth point data of the nautical chart to obtain vectorized data in shp format;

[0016] (c) Generate TIN based on water depth information in geographic information software, and then generate water depth raster;

[0017] (12) Prepare the land terrain data of the study area and construct a land-water integrated terrain model:

[0018] (a) Using the vector-to-raster tool in geographic information software, the vector area data of the 0m shoal was converted into raster data, and the raster value was set to 0m, which was approximated as the land topography of the shoal area;

[0019] (b) Using the Mosaic mosaic tool, the 0m shoal land topography and the water depth grid are merged to obtain the integrated land and water DEM data.

[0020] Furthermore, the specific steps of step 2 are:

[0021] (21) Construct the DEM translation model through matrix convolution operation;

[0022] (22) Based on the DEM translation model, the grids in a specific direction are translated to the central grid position in sequence, and the elevation angle EA value of the central grid relative to all the traversed neighboring grids in the specific direction is calculated and compared with each other to take the maximum value LEA. The positive sign indicates that the central grid is above the neighboring grid, and the negative sign indicates that it is below.

[0023] (23) Compare the LEAs obtained in multiple specific directions and take the maximum value as the three-dimensional minimum elevation angle 3D LEA.

[0024] Furthermore, the specific steps of step 3 are:

[0025] (31) Clipping 3D LEA raster data based on 0m shoal area vector to obtain 3D LEA raster data of land area;

[0026] (32) The terrestrial 3D LEA was reclassified at intervals of 5° to obtain the terrestrial 3D LEA distribution map, which was used as a quantitative indicator of bank collapse risk.

[0027] Furthermore, the specific steps of step 4 are:

[0028] (41) extracting the grid part where the 3D LEA is greater than the threshold ST, and converting the grid into district vector data as the range of bank collapse;

[0029] (42) Extract the grid part where the 3D LEA is less than 0.5 times the ST, and convert the grid into district vector data as the area outside the bank collapse range;

[0030] (43) The grid part where the 3D LEA is greater than 0.5 times the ST and less than the ST is extracted, and the grid is converted into district vector data as the bank collapse transition zone.

[0031] Furthermore, an approximate construction method of the integrated land and water DEM model is proposed; specifically, the underwater terrain uses the water depth data, and the land terrain sets the elevation of the 0m shoal to 0m, and then performs the splicing operation.

[0032] Furthermore, the calculation method of 3D LEA adopts a multi-directional traversal mode. Specifically, the number of directions of the multi-directional traversal is 4-360, and the segmentation interval is 1-90°.

[0033] Furthermore, the neighborhood grid range considered for 3D LEA calculation is within 1000 m.

[0034] Furthermore, the critical value ST of the bank collapse range predicted by 3D LEA is determined according to the critical slope of the underwater bank slope, and the ST value is between 5 and 25°.

[0035] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: By using the integrated land and water DEM to calculate 3D LEA, the present invention provides quantitative indicators for the characterization of underwater landform beach intrusion risks, predicts the scope of bank collapse, and is expected to assist in determining the boundary position of shoreline planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of the bank collapse range prediction method based on the integrated water-land elevation angle;

[0037] Figure 2 It is a schematic diagram of the results of implementing the vectorization and rasterization of the sample area chart data;

[0038] Figure 3 It is a schematic diagram of the water-land integrated digital elevation model data for the sample area;

[0039] Figure 4 This is a schematic diagram of the principle of predicting the range of bank collapse based on the integrated water-land elevation angle;

[0040] Figure 5 It is a schematic diagram of multi-directional traversal mode (a) and elevation angle calculation (b);

[0041] Figure 6 This is the 3D LEA extraction result map;

[0042] Figure 7 It is a 3D LEA classification map on land;

[0043] Figure 8 This is a distribution map of the scope of landslides. DETAILED DESCRIPTION

[0044] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0045] The present invention proposes a method for predicting the range of bank collapse based on the integrated water-land elevation angle. Figure 1 As shown, the specific steps include:

[0046] Step 1: Construct an integrated underwater and land digital elevation model (DEM) of the study area;

[0047] DEM (Digital Elevation Model) is the abbreviation of digital elevation model. It is a digital expression of the terrain surface and contains rich terrain and geomorphological information.

[0048] The DEM data of the study area comes from the integrated land and water terrain data of the Hengsha channel in the Yangtze River Estuary, located at N31°21′1″-N31°23′, E121°46′-E121°49′. The data is high-resolution DEM data for navigation chart production with a resolution of 5m. The data is divided into 8 periods, namely 1984, 1992, 2001, 2005, 2009, 2011, 2014 and 2017, as shown in Table 1. The vectorized shoal, coastline and rasterized water depth data of the experimental sample area navigation chart are shown in Table 1. Figure 2 shown.

[0049] Table 1 Summary of sources of nautical chart data in the study area

[0050] Table 1 Summary of sources of nautical chart data in the study area

[0051]

[0052] The steps to produce underwater topographic data of the study area are:

[0053] 1) According to the longitude and latitude range of the Yangtze River Estuary research area, the 3-degree zone of the Beijing 1954 Gausskrüger projection was selected in ArcGIS10.3 to establish a spatial coordinate system, and the base map data was registered with this coordinate system in combination with the longitude and latitude information of the nautical chart to obtain the geographic coordinate position data;

[0054] 2) Digitalize the shoreline, isobaths (0m, -2m, -5m, -10m, -20m, -30m, -40m, -50m) and water depth point data in the nautical chart to obtain vectorized data in shp format;

[0055] 3) Generate TIN based on water depth information in ArcGIS, and then generate water depth raster. Figure 2 shown.

[0056] The steps to construct the integrated land and water DEM data of the study area are:

[0057] 1) Using the vector-to-raster tool in ArcGIS, the vector area data of the 0m shoal was converted into raster data, and the raster value was set to 0m, which was approximated as the land topography of the shoal area;

[0058] 2) Using the Mosaic mosaic tool, the 0m shoal land topography and the water depth grid were merged to obtain the integrated land and water DEM data.

[0059] Step 2: Based on the matrix convolution operation, a translation model of the DEM is constructed, and the three-dimensional minimum elevation angle (3D LEA) is calculated from the DEM based on the translation model;

[0060] The principle of predicting the scope of bank collapse based on the integrated water-land elevation angle is as follows: Figure 4 Different distances from the shore have different sight angles, which can be described by the size of the minimum elevation angle (LEA), corresponding to different bank collapse risks, such as Figure 4 (a) shows that the nearshore is an object in three-dimensional space, and it is necessary to find the maximum LEA on multiple sections, such as Figure 4 (b) and (c). The LEA of profile I is the largest, which is the lowest elevation angle in three-dimensional space, namely 3D LEA.

[0061] In the actual calculation process, 3D LEA is obtained by comparing the elevation angles of the central grid relative to all the neighborhood grids and taking the maximum value. The specific calculation of 3D LEA consists of three parts, namely:

[0062] 1) Based on matrix convolution operation, construct a DEM translation model, such as Figure 5 (a)

[0063] 2) Use multiple specific direction translations to calculate the elevation angle EA of the central grid relative to each neighboring grid in each direction; the calculation of the elevation angle is as follows: Figure 5 (b), upward is positive and downward is negative;

[0064] 3) Compare the elevation angles of the neighboring grids traversed in a specific direction, and take the maximum value as the lowest elevation angle LEA in that specific direction; compare the LEAs in multiple specific directions, and take the maximum value as the three-dimensional lowest elevation angle 3D LEA. The results are as follows: Figure 6 shown.

[0065] Step 3: Clip the 3D LEA based on the part above the 0m line to obtain the 3D LEA distribution of the land area as a quantitative indicator of bank collapse risk. The specific steps are as follows:

[0066] 1) Using ArcGIS clipping tools, the 3D LEA raster data was clipped based on the 0m shoal area vector to obtain the 3D LEA raster data of the land area;

[0067] 2) Reclassify the land 3D LEA at intervals of 5° to obtain the land 3D LEA distribution map, as shown in Figure 7 shown.

[0068] Step 4: Set the critical terrain slope ST of the underwater bank slope and predict the bank collapse range based on the relative size of 3D LEA and ST. The specific steps are as follows:

[0069] 1) Using the raster calculator, extract the raster part where the 3D LEA is greater than the threshold ST, and convert the raster into district vector data as the range of bank collapse;

[0070] 2) Using the raster calculator, extract the raster portion where the 3D LEA is less than 0.5 times the ST, and convert the raster into district vector data as outside the bank collapse range;

[0071] 3) Using the grid calculator, extract the grid part where the 3D LEA is greater than 0.5 times ST and less than ST, and convert the grid into district vector data as the bank collapse transition zone, such as Figure 8 shown.

[0072] Figure 8 The figure shows the predicted results of bank collapse range when ST is set to 10°. As can be seen from the figure, as the riverbed scouring near the northern entrance of Hengsha Channel intensifies and the underwater bank slope becomes steeper, the predicted range of bank collapse near the shore increases, indicating that the scale of potential bank collapse increases. In addition, the predicted results of the bank collapse range of the present invention have a good correlation with the shoreline planning position. Figure 8 As shown in (a)-(d), the scour pit gradually formed and the predicted bank collapse range gradually expanded. By 2005, it intersected with the planned coastline. Then in 2009, the coastline near the scour pit retreated for the first time. Figure 8 As shown in (e)-(h), the underwater scour pit continues to deepen, and the predicted bank collapse range continues to expand. By 2017, the predicted bank collapse range will intersect with the planned coastline again, so the planned coastline is expected to retreat again. This invention is expected to assist in nearshore land use planning. The present invention uses the underwater topography as the basis for predicting the range of bank collapse on land, constructs a water-land integrated digital elevation model, proposes to simulate the potential sliding surface through line of sight, simulates the slope ratio of the bank after potential bank collapse through elevation angle, and quantifies the risk of bank collapse at different distances from the water. Compared with the existing bank collapse prediction results, the present invention can quantify the relationship between the risk of bank collapse and the distance from the water, and realizes the prediction of the range of bank collapse in a two-dimensional plane.

Claims

1. A method for predicting the range of bank collapse based on the integrated water-land elevation angle, characterized in that: The steps include: Step 1: Construct an integrated underwater and land digital elevation model (DEM) of the study area; Step 2: Based on the matrix convolution operation, a translation model of the DEM is constructed, and the three-dimensional minimum elevation angle (3D LEA) is calculated from the DEM based on the translation model; Step 3: Construct the shallow area below 0 m, perform mask analysis on the 3D LEA, and obtain the 3D LEA grid data of the land part as a quantitative indicator of bank collapse risk; Step 4: Set the angle of repose ST of the underwater slope as the threshold, reclassify the 3D LEA, and use the high-value area as the prediction range of bank collapse.

2. The method for predicting the range of bank collapse based on the integrated water-land elevation angle according to claim 1 is characterized in that: The specific steps of step 1 are: (11) Prepare underwater topographic data of the study area: (a) According to the latitude and longitude range of the study area, a spatial coordinate system is established in the geographic information software, and the base map data is aligned with the coordinate system in combination with the latitude and longitude information of the nautical chart to obtain the geographic coordinate position data; (b) digitize the shoreline, depth contours and water depth point data of the nautical chart to obtain vectorized data in shp format; (c) Generate TIN based on water depth information in geographic information software, and then generate water depth raster; (12) Prepare the land terrain data of the study area and construct a land-water integrated terrain model: (a) Using the vector-to-raster tool in geographic information software, the vector area data of the 0m shoal was converted into raster data, and the raster value was set to 0m, which was approximated as the land topography of the shoal area; (b) Using the Mosaic mosaic tool, the 0m shoal land topography and the water depth grid are merged to obtain the integrated land and water DEM data.

3. The method for predicting the range of bank collapse based on the integrated water-land elevation angle according to claim 1 is characterized in that: The specific steps of step 2 are: (21) Construct the DEM translation model through matrix convolution operation; (22) Based on the DEM translation model, the grids in a specific direction are translated to the central grid position in sequence, and the elevation angle EA value of the central grid relative to all the traversed neighboring grids in the specific direction is calculated and compared with each other to take the maximum value LEA. The positive sign indicates that the central grid is above the neighboring grid, and the negative sign indicates that it is below. (23) Compare the LEAs obtained in multiple specific directions and take the maximum value as the three-dimensional minimum elevation angle 3D LEA.

4. The method for predicting the range of bank collapse based on the integrated water-land elevation angle according to claim 1 is characterized in that: The specific steps of step 3 are: (31) Clipping 3D LEA raster data based on 0m shoal area vector to obtain 3D LEA raster data of land area; (32) The terrestrial 3D LEA was reclassified at intervals of 5° to obtain the terrestrial 3D LEA distribution map, which was used as a quantitative indicator of bank collapse risk.

5. The method for predicting the range of bank collapse based on the integrated water-land elevation angle according to claim 1 is characterized in that: The specific steps of step 4 are: (41) extracting the grid part where the 3D LEA is greater than the threshold ST, and converting the grid into district vector data as the range of bank collapse; (42) Extract the grid part where the 3D LEA is less than 0.5 times the ST, and convert the grid into district vector data as the area outside the bank collapse range; (43) The grid part where the 3D LEA is greater than 0.5 times the ST and less than the ST is extracted, and the grid is converted into district vector data as the bank collapse transition zone.

6. The method for predicting the range of bank collapse based on the integrated water-land elevation angle according to claim 1 is characterized in that: An approximate construction method for the integrated land and water DEM model; specifically, the underwater terrain uses water depth data, and the land terrain sets the 0m shoal elevation to 0m, and then performs splicing operations.

7. The method for predicting the range of bank collapse based on the integrated land-water elevation angle according to claim 1 is characterized in that: The 3DLEA calculation method adopts a multi-directional traversal mode; specifically, the number of directions of the multi-directional traversal is 4-360, and the segmentation interval is 1-90°.

8. The method for predicting the range of bank collapse based on the integrated water-land elevation angle according to claim 1 is characterized in that: The neighborhood grid range considered in 3DLEA calculation is within 1000m.

9. The method for predicting the range of bank collapse based on the integrated water-land elevation angle according to claim 1 is characterized in that: The critical value ST of the bank collapse range predicted by 3DLEA is determined according to the critical slope of the underwater bank slope, and the ST value is between 5-25°.

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