A method for planarization of discrete point data considering terrain influence

Abstract

The present invention discloses a planarization method for discrete point data considering the influence of terrain, including extraction of ridge characteristic lines based on spatial scale; selection of interpolation sites taking into account ridge lines and search fields; and planarization processing using a general interpolation method based on the premise of ridge characteristic lines and interpolation sites for discrete point data. The present invention is applicable to the technical field of gridding meteorological elements and related indexes of discrete points. By extracting ridge characteristic lines and selecting interpolation sites, and then planarizing the discrete point data using an inverse distance weighted interpolation method based on the premise of the two, the problem of neglecting the influence of terrain in the existing methods is solved, the limitations of the existing methods are overcome, terrain factors are reasonably selected, and the versatility is high. In addition, the present invention also calculates the two positional relationships between the spatial distribution of sites and the mountains, thereby studying the completely different climate characteristics on both sides of the mountains, and has high practical value.

Description

Technical Field

[0001] The invention belongs to the technical field of gridding meteorological elements and related indexes of discrete points, and specifically is a method for planarizing discrete point data taking into account the influence of terrain. Background Art

[0002] Topography is one of the main factors affecting the regional distribution differences of meteorological elements. The types of terrain, elevation, mountain distribution, mountain orientation, and windward and leeward slopes not only determine the distribution differences of meteorological elements such as precipitation, temperature, wind speed, humidity, sunshine, and radiation, but also further affect the distribution differences of meteorological correlation coefficients and indices. Therefore, in the plane interpolation of meteorological discrete point data, how to take into account the impact of terrain on regional distribution differences is a common and important issue.

[0003] However, the existing planarization of discrete point data directly incorporates terrain factors such as altitude, slope, and aspect into plane interpolation, and considers the impact of terrain on the regional distribution differences of meteorological elements by establishing regression equations between target variables and factors such as altitude, slope, and aspect. This type of method considers terrain factors more directly, but the selection of terrain factors is more arbitrary and has poor versatility, so it has great limitations. In addition, the existing method does not consider the segmentation effect of mountains, has great limitations, and has low practical value. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the prior art and provide a method for planarizing discrete point data taking into account the influence of terrain.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for planarizing discrete point data considering the influence of terrain, comprising:

[0007] Extraction of ridge feature lines based on spatial scale;

[0008] Selection of interpolation sites taking into account ridgelines and search areas;

[0009] Plane interpolation of discrete point data: Based on the premise of ridge characteristic lines and interpolation stations, general interpolation methods are used for planarization.

[0010] Preferably, the general interpolation method is an inverse distance weighted interpolation method:

[0011] Assume that the value μ(X) at point X is based on μ i =u(x i ) samples, where i = 1, 2, ... N, then the inverse distance weighted interpolation function satisfies: μ i =μ(x i )

[0012]

[0013]

[0014] Among them, X represents the interpolated arbitrary point, Xi is the interpolated known point, d is the given distance from point Xi to point X, N is the total number of known points used in the interpolation, and p is a positive real number called the power parameter, which is usually 2.

[0015] Preferably, the weight ω i (x) decreases as the given distance from the known point Xi to the unknown point X increases.

[0016] Preferably, the ridge feature line extraction based on spatial scale includes: using the planarized grid data resolution as the anchor of the spatial scale; integrating the terrain data based on the anchor, unifying it to the resolution of the grid data, and using the integrated data as the basic data for ridge feature line extraction.

[0017] Preferably, the terrain data includes: altitude data, slope data and aspect data of different resolutions.

[0018] Preferably, the interpolation site selection taking into account the ridge line and the search area needs to meet two constraints: the boundary of the search area of ​​the similar site and the barrier of the ridge feature line.

[0019] Preferably, the boundary of the similar site search area refers to the similar sites of the grid points selected within a limit search distance.

[0020] Preferably, the solution to the obstruction of the ridge characteristic line is: if the search station and the interpolation grid point are on different sides relative to the ridge characteristic line, the station is not taken into consideration for interpolation, otherwise it is taken into consideration, wherein whether they are on the same side is determined by whether the line connecting the interpolation grid point and the station intersects with the ridge characteristic line.

[0021] Preferably, the limit search distance is 30 times the target grid data resolution.

[0022] Compared with the closest prior art, the technical solution provided by the present invention has the following beneficial effects:

[0023] The present invention extracts ridge feature lines and selects interpolation sites, and then performs planarization processing on discrete point data by using an inverse distance weighted interpolation method based on the two, thereby solving the problem of neglecting the influence of terrain in the existing methods, overcoming the limitations of the existing methods, reasonably selecting terrain factors, and having high versatility.

[0024] In addition, the present invention also calculates the two positional relationships between the spatial distribution of the sites and the mountains respectively, so as to study the completely different climate characteristics on both sides of the mountains, which has high practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of a method for planarizing discrete point data taking into account the influence of terrain according to the present invention;

[0026] Figure 2 It is a schematic diagram of the distribution of ridge characteristic lines at a scale of 9 km in a discrete point data planarization method considering the influence of terrain in the present invention;

[0027] Figure 3 This is a schematic diagram of the radiation stations located on one side of the same ridge line in the preferred embodiment 2 of the present invention;

[0028] Figure 4 This is a schematic diagram of the radiation stations located on both sides of the same ridge line in the preferred embodiment 3 of the present invention. DETAILED DESCRIPTION

[0029] The following is combined with Figure 1-4 , further illustrating a specific implementation of a discrete point data planarization method considering terrain influence of the present invention. A discrete point data planarization method considering terrain influence of the present invention is not limited to the description of the following embodiments.

[0030] Embodiment 1:

[0031] This embodiment provides a specific structure of a method for planarizing discrete point data taking into account the influence of terrain. Figure 1 and Figure 2 As shown, including:

[0032] Extraction of ridge feature lines based on spatial scale;

[0033] Selection of interpolation sites taking into account ridgelines and search areas;

[0034] Plane interpolation of discrete point data: Based on the premise of ridge characteristic lines and interpolation stations, general interpolation methods are used for planarization.

[0035] Furthermore, the general interpolation method is the inverse distance weighted interpolation method:

[0036] Assume that the value μ(X) at point X is based on μ i =μ(x i ) samples, where i = 1, 2, ... N, then the inverse distance weighted interpolation function satisfies: μ i =μ(x i )

[0037]

[0038]

[0039] Among them, X represents the interpolated arbitrary point, Xi is the interpolated known point, d is the given distance from point Xi to point X, N is the total number of known points used in the interpolation, and p is a positive real number called the power parameter, which is usually 2.

[0040] Furthermore, the weight ω i (x) decreases as the given distance from the known point Xi to the unknown point X increases.

[0041] Furthermore, the extraction of ridge feature lines based on spatial scale includes: using the planarized grid data resolution as the anchor of the spatial scale; integrating the terrain data based on the anchor, unifying it to the resolution of the grid data, and using the integrated data as the basic data for ridge feature line extraction.

[0042] Furthermore, the terrain data includes: elevation data, slope data and aspect data of different resolutions.

[0043] Specifically, the selection of interpolation sites that take into account the ridgeline and the search field must meet two constraints: the boundary of the search field of the similar sites and the barrier of the ridge feature line.

[0044] Specifically, the boundary of the close site search area refers to the close sites of the selected grid points within a limit search distance.

[0045] Specifically, the solution to the obstruction of the ridge characteristic line is: if the search station and the interpolation grid point are on different sides relative to the ridge characteristic line, the station will not be taken into account for interpolation, otherwise it will be taken into account, and whether they are on the same side is determined by whether the line connecting the interpolation grid point and the station intersects with the ridge characteristic line.

[0046] Specifically, the limit search distance is 30 times the target grid data resolution.

[0047] How it works

[0048] Ridge feature line extraction based on spatial scale:

[0049] For different spatial scales, due to the different data granularity, the meteorological distribution characteristics and terrain characteristics are different. The wind speed distribution and temperature distribution at a larger spatial scale are different from those at a smaller scale. Similarly, the mountain characteristics at a larger spatial scale are also significantly different from those at a smaller scale. In the process of flattening discrete point data, the impact of terrain must take into account the difference in spatial scale and match it with the spatial scale of meteorological elements.

[0050] Based on the above considerations, the resolution of the planarized grid data is used as the anchor of the spatial scale, and this is used as the standard for data integration of terrain data. We integrate the elevation data, slope data, and aspect data of different resolutions and unify them to the resolution of the grid data, which is used as the basic data for extracting ridge feature lines.

[0051] Interpolation site selection taking into account ridgelines and search areas:

[0052] According to the first law of geography, everything is related, and things that are close are more closely related. The usual interpolation method is to judge the degree of "closeness" of the stations and use this as a basis to calculate the value of the target grid point based on the values ​​of the nearby stations;

[0053] The interpolation methods of meteorological elements have different strategies in judging the degree of “closeness”. Here, we consider the particularity of the spatial distribution of meteorological elements and propose two “hard constraints”: the boundary of the search area of ​​similar sites and the barrier of the ridge feature line;

[0054] In the operation, the boundary of the search area of ​​the close site refers to the limit search distance we choose when selecting the adjacent sites of the grid point. We use 30 times the data resolution of the target grid point in the business;

[0055] We adopt the following strategy for the obstruction of ridge characteristic lines: if the search station and the interpolation grid point are on different sides relative to the ridge characteristic line, the station will not be considered for interpolation; otherwise, it will be considered. Whether they are on the same side is determined by whether the line connecting the interpolation grid point and the station intersects the ridge characteristic line.

[0056] Plane interpolation of discrete point data:

[0057] Based on the premise of ridge feature lines and interpolation stations, the discrete point data is planarized using the inverse distance weighted interpolation method: Assume that the value μ(X) at point X is based on μ i =μ(x i ) samples, where i = 1, 2, ... N, then the inverse distance weighted interpolation function satisfies: μ i =μ(x i )

[0058]

[0059]

[0060] Where X represents an arbitrary interpolated point, Xi is a known interpolated point, d is the given distance from point Xi to point X, N is the total number of known points used in the interpolation, and p is a positive real number called the power parameter, which is usually 2;

[0061] The discrete point data planarization process is completed.

[0062] Embodiment 2:

[0063] This embodiment provides a specific structure of a method for planarizing discrete point data taking into account the influence of terrain. Figure 3 As shown, the station is located on one side of the ridge line: This situation is more common. The grid point S is within the rectangle surrounded by nodes ABCD. Therefore, the four nodes ABCD have an impact on the grid point S. The value of the radiation grid point S is obtained by performing inverse distance weighted interpolation on the four points ABCD. The formula is as follows:

[0064]

[0065] The value of the radiation grid point S can be obtained when the station is located on one side of the ridge line.

[0066] Embodiment 3:

[0067] This embodiment provides a specific structure of a method for planarizing discrete point data taking into account the influence of terrain. Figure 4 As shown in the figure, the stations are located on both sides of the ridgeline: if the grid point is on the left side of the ridgeline, the station on the left is used for calculation; if the grid point is on the right side of the ridgeline, the station on the right is used for calculation. The following is one of the cases, where line L is the ridgeline, stations A, B, D and the grid point s to be interpolated are located on one side of the ridgeline, and the calculation formula is as follows:

[0068]

[0069] The value of the radiation grid point S can be obtained when the station is located on both sides of the ridge line.

[0070] The present invention extracts ridge feature lines and selects interpolation sites, and then performs planarization processing on discrete point data by using an inverse distance weighted interpolation method based on the two, thereby solving the problem of neglecting the influence of terrain in the existing methods, overcoming the limitations of the existing methods, reasonably selecting terrain factors, and having high versatility.

[0071] In addition, the present invention also calculates the two positional relationships between the spatial distribution of the sites and the mountains respectively, so as to study the completely different climate characteristics on both sides of the mountains.

[0072] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A method for planarizing discrete point data considering the influence of terrain, characterized in that ,include: Extraction of ridge feature lines based on spatial scale; Interpolation site selection taking into account ridgelines and search areas; Plane interpolation of discrete point data: Based on the premise of ridge feature lines and interpolation sites, the inverse distance weighted interpolation method is used for planarization; The extraction of ridge feature lines based on spatial scale includes: using the planarized grid data resolution as an anchor of spatial scale, integrating terrain data based on the anchor, unifying the data to the resolution of the grid data, and using the integrated data as basic data for ridge feature line extraction; The interpolation site selection considering the ridge line and the search area must meet two constraints: the boundary of the search area of ​​the adjacent site and the barrier of the ridge feature line; The solution to the obstruction of the ridge characteristic line is: if the search station and the interpolation grid point are on different sides relative to the ridge characteristic line, the station is not included in the interpolation consideration, otherwise it is considered, and whether they are on the same side is determined by whether the line connecting the interpolation grid point and the station intersects with the ridge characteristic line.

2. A method for planarizing discrete point data considering terrain influence as claimed in claim 1, characterized in that: The inverse distance weighted interpolation method is: Assume that the value at point X is is based on Sample, where i=1, 2, ...N, then the inverse distance weighted interpolation function satisfies: Among them, X represents the interpolated arbitrary point, Xi is the interpolated known point, d is the given distance from point Xi to point X, N is the total number of known points used in the interpolation, and p is a positive real number called the power parameter.

3. A method for planarizing discrete point data considering terrain influence as claimed in claim 2, characterized in that: The weight It decreases as the given distance from the known point Xi to the unknown point X increases.

4. A method for planarizing discrete point data considering terrain influence as claimed in claim 1, characterized in that: The terrain data includes: altitude data, slope data and aspect data of different resolutions.

5. The method for planarizing discrete point data considering terrain influence as claimed in claim 1, characterized in that: The boundary of the similar site search area refers to the similar sites of the grid points selected within a limit search distance.

6. A method for planarizing discrete point data considering terrain influence as claimed in claim 5, characterized in that: The limit search distance is 30 times the target grid data resolution.

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