A water depth inversion method based on measured terrain and remote sensing data

Abstract

The application provides a water depth inversion method based on measured terrain and remote sensing data, which comprises the following steps: acquiring remote sensing image data and measured terrain data; preprocessing the remote sensing image data and the measured terrain data to obtain preprocessed remote sensing image data and preprocessed measured terrain data; and performing data operation processing on the preprocessed remote sensing data and the preprocessed measured terrain data to obtain water depth. The method can not only realize the inversion of past-present-future water depth in a relatively stable underwater terrain area, but also realize the inversion of spatial water depth.

Description

Technical Field

[0001] This invention relates to the field of water depth inversion, and more particularly to a water depth inversion method based on measured topographic data and remote sensing data. Background Technology

[0002] Currently, there are many and diverse algorithms for river depth inversion. Most of them use machine learning or different linear / nonlinear models to calculate river depth based on different remote sensing image bands, which have local applicability. On the one hand, whether the existing methods can be applied to the inversion of water depth in the same area at different times needs further investigation. On the other hand, there are some parts that are difficult to explain when the methods reveal water depth inversion based on principles. Water depth inversion algorithms in one area are difficult to promote and apply in other areas, so their applicability is limited. Summary of the Invention

[0003] In view of the above problems, the present invention is proposed to provide a water depth inversion method based on measured topography and remote sensing data to overcome or at least partially solve the above problems.

[0004] According to one aspect of the present invention, a method for water depth inversion based on measured topography and remote sensing data is provided, comprising:

[0005] Acquire remote sensing image data and measured terrain data;

[0006] The remote sensing image data and the measured terrain data are preprocessed to obtain preprocessed remote sensing image data and preprocessed measured terrain data;

[0007] The water depth is obtained by performing data processing on the preprocessed remote sensing data and the preprocessed measured topographic data.

[0008] Optionally, the preprocessing of the remote sensing image data specifically includes:

[0009] Based on the NDWI index of the remote sensing image data, the image raster is divided into river and non-river types, with river rasters assigned a value of 0 and non-river rasters assigned a value of 1.

[0010] Optionally, the preprocessing of the measured terrain data specifically includes:

[0011] In the ArcGIS platform, the measured terrain data is converted into raster data of corresponding resolution through interpolation, and the raster layer is filled with depressions and the flow direction is calculated.

[0012] The flow direction results are represented by 1, 2, 4, 8, 16, 32, 64, and 128, which represent due east, 45° east of south, due south, 45° south of west, due west, 45° west of north, due north, and 45° north of east, respectively.

[0013] The grid cells are numbered sequentially, row i, column j, from left to right as column 1, column 2, ..., column j, and from top to bottom as row 1, row 2, ..., row i.

[0014] The process involves moving from left to right and then from top to bottom, starting with the first grid cell in the top left corner to determine whether the classification result is river 0 or non-river 1.

[0015] Optionally, the step of proceeding from left to right and then from top to bottom, starting from the first grid cell in the upper left corner, to determine whether the classification result is river 0 or non-river 1, specifically includes:

[0016] According to Bernoulli's equation, when the flow velocity is the same and head loss is ignored, adjacent grids in the direction perpendicular to the river flow direction have the same water level elevation.

[0017] If the result is non-river 1, proceed to the next grid cell. If the result is river 0, further determine the river flow direction of that grid cell. There are four possible outcomes:

[0018] If the river flows in the direction of 1 or 16, then the grid position that corresponds to the water level elevation of the corresponding grid is perpendicular to the direction of 1 or 16, and the number of rows and columns is (i+1,j) and (i-1,j).

[0019] If the river flows in the direction of 4 or 64, then the grid position that matches the water level elevation of this grid is perpendicular to the flow direction of 4 or 64, and the number of rows and columns is (i,j-1) and (i,j+1).

[0020] If the river flows in the direction of 8 or 128, then the grid position that matches the water level elevation of this grid is perpendicular to the flow direction of 8 or 128, and the number of rows and columns is (i-1, j-1) and (i+1, j+1).

[0021] If the river flows in a direction of 2 or 32, then the grid position that matches the water level elevation of that grid is perpendicular to the flow direction of 2 or 32, and the number of rows and columns is (i+1,j-1) and (i-1,j+1).

[0022] Optionally, the data processing of the preprocessed remote sensing data and the preprocessed measured terrain data specifically includes:

[0023] The water depth of the target grid is determined by combining the grid type of the water level elevation and the terrain data, and the water depth result is obtained.

[0024] The water depth results are assigned to the grid attributes, and this process is repeated continuously until all grids within the study area are calculated, thus achieving regional water depth calculation.

[0025] This invention provides a water depth inversion method based on measured topography and remote sensing data, comprising: acquiring remote sensing image data and measured topography data; preprocessing the remote sensing image data and the measured topography data to obtain preprocessed remote sensing image data and preprocessed measured topography data; and performing data processing on the preprocessed remote sensing data and the preprocessed measured topography data to obtain the water depth. This method can not only invert the time-series water depth in areas with relatively stable underwater topography (past-present-future time series), but also invert the spatial water depth.

[0026] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a schematic diagram of the flow direction numbering for a water depth inversion method based on measured topography and remote sensing data provided by the present invention.

[0029] Figure 2 This is a schematic diagram illustrating the location numbering of a water depth inversion algorithm based on measured terrain and remote sensing data provided by the present invention.

[0030] Figure 3 This invention provides a schematic diagram of a water depth inversion algorithm based on measured terrain and remote sensing data. Detailed Implementation

[0031] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0032] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0033] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0034] A water depth inversion method based on measured topography and remote sensing data, including preprocessed data such as remote sensing image data and measured topography data.

[0035] Based on the NDWI index of the remote sensing image, the image raster is divided into river and non-river raster, with river raster assigned a value of 0 and non-river raster assigned a value of 1.

[0036] The preprocessing steps for terrain data include: converting the measured terrain point data into raster data of the corresponding resolution through interpolation in the ArcGIS platform; filling depressions in the raster layer and calculating the flow direction. The flow direction results are represented by 1, 2, 4, 8, 16, 32, 64, and 128, such as... Figure 1 As shown, the directions of flow are due east, 45° east of south, due south, 45° south of west, due west, 45° west of north, due north, and 45° north of east.

[0037] The grid cells are numbered sequentially, with row i and column j, from left to right as column 1, column 2, ..., column j, and from top to bottom as row 1, row 2, ..., row i. Considering that one grid is surrounded by eight grids, there are eight different flow directions. To better describe the positional relationship between the calculated grid and its adjacent grids, we assume that the grid's own position code is represented by (i, j), the grid position code for flow direction 1 is represented by (i, j+1), the grid position code for flow direction 2 is represented by (i+1, j+1), the grid position code for flow direction 4 is represented by (i+1, j), the grid position code for flow direction 8 is represented by (i+1, j-1), and the grid position code for flow direction 16 is represented by (i, j-1). This means that the grid position code for flow direction 32 is represented by (i-1, j-1), the grid position code for flow direction 64 is represented by (i-1, j), and the grid position code for flow direction 128 is represented by (i-1, j+1). Figure 2 As shown.

[0038] The process proceeds from left to right and then from top to bottom, starting with the first grid cell in the top left corner, determining whether it is classified as a river (0) or a non-river (1). According to Bernoulli's equation, with the same flow velocity and neglecting head loss, the water head is the same, and adjacent grid cells perpendicular to the river flow direction have the same water level elevation. If the result is not river 1, proceed to the next grid cell. If the result is river 0, further determine the river flow direction of that grid cell. There are four cases: If the river flow direction is 1 or 16, the grid cell position with the corresponding grid water level elevation is perpendicular to the flow direction 1 or 16, with row and column numbers of (i+1, j) and (i-1, j); if the river flow direction is 4 or 64, the grid cell position with the corresponding grid water level elevation is perpendicular to the flow direction 4 or 64, with row and column numbers of (i, j-1) and (i, j+1); if the river flow direction is 8 or 128, the grid cell position with the corresponding grid water level elevation is perpendicular to the flow direction 8 or 128, with row and column numbers of (i-1, j-1) and (i+1, j+1); if the river flow direction is 2 or 32, the grid cell position with the corresponding grid water level elevation is perpendicular to the flow direction 2 or 32. 32, the number of rows and columns is (i+1, j-1) and (i-1, j+1).

[0039] The water depth of the target grid is determined by combining the grid type of the isostatic elevation and the terrain data. Since there are only two types of region classification results, river 0 and non-river 1, there are two grids adjacent to the calculation grid in the vertical flow direction. Assuming that A and B are the location information of the two grids adjacent to the calculation grid in the vertical flow direction, there are three possibilities for the sum of the classification results: (1) When the sum of the classification results of the two grids adjacent to the calculation grid in the vertical flow direction is equal to 2, both grids at A and B are non-river. If the DEMs of the two grids at A and B are consistent, then the water depth here = DEM(A or B) - DEM(calculation point). If the DEMs of the two grids at A and B are inconsistent, then the water depth here = [DEM(A) + DEM(B)] * 0.5 - DEM(calculation point). Assign the water depth result to the grid attribute; (2) When the sum of the classification results of the two grids adjacent to the calculation grid in the vertical flow direction is equal to 1, it means that one of the two grids at A and B is a river and the other is a non-river. At this time, the water depth = DEM(non-river grid) - DEM (Calculation point), assign the water depth result to the grid attribute; (3) When the sum of the classification results of the two grids adjacent to the calculation grid in the vertical flow direction is equal to 0, both grids at A and B are rivers. If the water depth of grids at A and B has not been calculated, then NoData is assigned to the water depth of that point. If one of the grids at A and B has been calculated, then water depth = DEM(grid with calculated water depth) + water depth(grid with calculated water depth) - DEM(calculation point). If the water depth of grids at A and B has been calculated, then it is necessary to determine whether the water surface elevation (i.e., water depth + DEM) of grids at A and B is consistent. If consistent, water depth calculation = DEM(A) + water depth(A) - DEM(calculation point). If inconsistent, water depth = [DEM(A) + water depth(A) + DEM(B) + water depth(B)] * 0.5 - DEM(calculation point). Finally, the water depth results are assigned to the raster attributes, and this process is repeated continuously until all raster data within the study area is calculated, thus achieving regional water depth calculation. This method can be used to invert water depth for rivers with minimal or no changes in underwater topography over the years, realizing both spatial and temporal water depth inversion.

[0040] Example 1

[0041] This invention is a water depth inversion algorithm based on measured topography and remote sensing data. The design includes two parts: data preprocessing and data processing. The data required includes remote sensing images and measured topography data of the study area, either point features or raster files.

[0042] In this implementation case, the data preprocessing stage first involves interpreting and classifying the remote sensing images. After preprocessing, the study area is divided into river and non-river categories based on the NDWI water index, with rivers assigned a value of 0 and non-river assigned a value of 1. The measured topographic data in point data format is then spatially interpolated and converted to raster format. ArcGIS is used for hydrological analysis, including depression filling and flow direction calculations. The flow direction results are represented by 1, 2, 4, 8, 16, 32, 64, and 128. Figure 1 As shown, the flow directions are due east, 45° east of south, due south, 45° south of west, due west, 45° west of north, due north, and 45° north of east. The grid cells are numbered in rows i and columns j, from left to right as column 1, column 2...j, and from top to bottom as row 1, row 2...row i. Considering that one grid is surrounded by eight grids, meaning there are eight different flow directions, to better describe the positional relationship between the calculated grid and its adjacent grids, we assume that the grid itself is encoded. Let (i, j) represent the raster position codes for flow direction 1, flow direction 2, flow direction 4, flow direction 8, flow direction 16, flow direction 32, flow direction 64, and flow direction 128. For more details, please refer to [reference needed]. Figure 2 .

[0043] In this implementation, to avoid missing any grid cells during the data processing, a left-to-right, then top-to-bottom approach is adopted. Starting with the first grid cell in the top left corner, the classification result is determined as either "river 0" or "non-river 1". According to Bernoulli's equation, with the same flow velocity and neglecting head loss, the water head is the same; that is, adjacent grid cells perpendicular to the river flow direction have the same water level elevation. If the result is "non-river 1", the process continues to the next grid cell.

[0044] If the result is river 0, then the river flow direction of the grid is further determined. There are four cases: if the river flow direction is 1 or 16, then the grid position with the same water level elevation is perpendicular to the flow direction 1 or 16, and the number of rows and columns is (i+1, j) and (i-1, j); if the river flow direction is 4 or 64, then the grid position with the same water level elevation is perpendicular to the flow direction 4 or 64, and the number of rows and columns is (i, j-1) and (i, j+1); if the river flow direction is 8 or 128, then the grid position with the same water level elevation is perpendicular to the flow direction 8 or 128, and the number of rows and columns is (i-1, j-1) and (i+1, j+1); if the river flow direction is 2 or 32, then the grid position with the same water level elevation is perpendicular to the flow direction 2 or 32, and the number of rows and columns is (i+1, j-1) and (i-1, j+1).

[0045] The water depth of the target grid is determined by combining the grid type of the isostatic elevation and the terrain data. Since there are only two types of region classification results, river 0 and non-river 1, there are two grids adjacent to the calculation grid in the vertical flow direction. Assuming that A and B are the location information of the two grids adjacent to the calculation grid in the vertical flow direction, there are three possibilities for the sum of the classification results: (1) When the sum of the classification results of the two grids adjacent to the calculation grid in the vertical flow direction is equal to 2, both grids at A and B are non-river. If the DEMs of the two grids at A and B are consistent, then the water depth here = DEM(A or B) - DEM(calculation point). If the DEMs of the two grids at A and B are inconsistent, then the water depth here = [DEM(A) + DEM(B)] * 0.5 - DEM(calculation point). Assign the water depth result to the grid attribute; (2) When the sum of the classification results of the two grids adjacent to the calculation grid in the vertical flow direction is equal to 1, it means that one of the two grids at A and B is a river and the other is a non-river. At this time, the water depth = DEM(non-river grid) - DEM (Calculation point), assign the water depth result to the grid attribute; (3) When the sum of the classification results of the two grids adjacent to the calculation grid in the vertical flow direction is equal to 0, both grids at A and B are rivers. If the water depth of grids at A and B has not been calculated, then NoData is assigned to the water depth of that point. If one of the grids at A and B has been calculated, then water depth = DEM(grid with calculated water depth) + water depth(grid with calculated water depth) - DEM(calculation point). If the water depth of grids at A and B has been calculated, then it is necessary to determine whether the water surface elevation (i.e., water depth + DEM) of grids at A and B is consistent. If consistent, water depth calculation = DEM(A) + water depth(A) - DEM(calculation point). If inconsistent, water depth = [DEM(A) + water depth(A) + DEM(B) + water depth(B)] * 0.5 - DEM(calculation point).

[0046] Finally, the water depth results are assigned to the raster attributes, and this process is repeated until all raster data within the study area is calculated, thus achieving regional water depth calculation. This method can be used for time-series water depth inversion for rivers with stable riverbeds, and it can also be applied to water depth inversion for rivers with stable riverbeds in different spatial locations.

[0047] Beneficial effects: A water depth inversion method based on measured topography and remote sensing data was constructed, which can realize the inversion of water depth on a time scale in areas with relatively stable underwater topography, and has certain practical value.

[0048] The water depth inversion method based on measured topography and remote sensing data has broad application prospects. It can not only realize the inversion of past-present-future water depth in areas with relatively stable underwater topography, but also realize the inversion of spatial water depth, that is, the inversion of water depth in other areas with relatively stable underwater topography, laying a certain foundation for the study of habitat changes of fish, birds and amphibians.

[0049] The water depth inversion method based on measured topography and remote sensing data calculates water depth by starting with the relationship between water depth and water surface elevation and underwater topography, and has high universality in principle.

[0050] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A water depth inversion method based on measured terrain and remote sensing data, characterized in that, The inversion method comprises: acquiring remote sensing image data and measured terrain data; preprocessing the remote sensing image data and the measured terrain data to obtain preprocessed remote sensing image data and preprocessed measured terrain data; The preprocessing of the measured terrain data specifically comprises: in the Arcgis platform, the measured terrain data is converted into corresponding resolution raster data by interpolation, the raster layer is filled and the flow direction is calculated; the flow direction results are represented by 1, 2, 4, 8, 16, 32, 64 and 128, representing the flow directions of east, east-south 45°, south, west-south 45°, west, west-north 45°, north and north-east 45° respectively; the i rows and j columns of the raster units are numbered in turn, and from left to right, they are column 1, column 2, …, column j, and from top to bottom, they are row 1, row 2, …, row i; using the steps of first from left to right and then from top to bottom, the first raster in the top left corner is used to determine whether the classification result is a river 0 or a non-river 1, including: According to Bernoulli equation, the water head is the same under the condition of the same flow velocity and ignoring the water head loss, and the adjacent raster in the direction perpendicular to the river flow direction has the same water level elevation; If the result is non-river 1, the next raster is determined, if the result is river 0, the river flow direction of the raster is further determined, there are four cases: If the river flow direction is 1 or 16, the raster position consistent with the water level elevation of the corresponding raster is perpendicular to the flow direction 1 or 16, and the row and column numbers are (i+1, j) and (i-1, j); If the river flow direction is 4 or 64, the raster position consistent with the water level elevation of the corresponding raster is perpendicular to the flow direction 4 or 64, and the row and column numbers are (i, j-1) and (i, j+1); If the river flow direction is 8 or 128, the raster position consistent with the water level elevation of the corresponding raster is perpendicular to the flow direction 8 or 128, and the row and column numbers are (i-1, j-1) and (i+1, j+1); If the river flow direction is 2 or 32, the raster position consistent with the water level elevation of the corresponding raster is perpendicular to the flow direction 2 or 32, and the row and column numbers are (i+1, j-1) and (i-1, j+1); The data operation processing of the preprocessed remote sensing data and the preprocessed measured terrain data is carried out to obtain the water depth, specifically comprising: determining the water depth of the target raster in combination with the raster type of the equal water level elevation and the terrain data to obtain the water depth result; assigning the water depth result to the raster attribute, repeatedly circulating, and completing the calculation of all rasters in the research area to realize the calculation of the regional water depth. 2.The water depth inversion method based on measured terrain and remote sensing data according to claim 1, characterized in that, The preprocessing of the remote sensing image data specifically comprises: According to the NDWI index of the remote sensing image data, the image raster is divided into river and non-river, the river raster is assigned a value of 0, and the non-river raster is assigned a value of 1.

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