Quantitative characterization method for high-speed remote landslide motion path roughness

Abstract

The invention discloses a quantitative characterization method for the roughness degree of a high-speed remote landslide motion path, and belongs to the technical field of geological disasters, and the method comprises the steps: firstly obtaining remote sensing image data of a target landslide, DEM digital elevation data of the target landslide and landslide boundary data of the target landslide, and obtaining elevation data of the motion path of the target landslide through processing; carrying out detrending to obtain elevation data of the landslide motion path microtopography; and according to the elevation data of the landslide motion path microtopography, performing calculation based on a joint contour line fractal dimension calculation method to obtain the fractal dimension of the target landslide motion path after detrending, and performing calculation to obtain the path roughness coefficient of the target landslide. According to the method, the calculation process is simple, the application range is wide, the roughness degree of the high-speed remote landslide motion path can be quantified under different spatial scales, the influence of the microtopography in the landslide motion process is fully revealed, and more comprehensive roughness characterization is provided.

Description

Technical Field

[0001] The invention belongs to the technical field of geological disasters, and in particular relates to a quantitative characterization method for the roughness of a high-speed long-distance landslide movement path. Background Art

[0002] High-speed long-distance landslide refers to the geological phenomenon that large-scale bedrock on high and steep slopes in high mountain canyon areas lose stability and move long distances in the form of debris flow in a fluid-like state at a fast or extremely fast speed. This type of geological disaster has a highly concealed source area, sudden events and great hazards. Although its occurrence frequency is low, due to its own movement characteristics, it is affected by the terrain during high-speed movement and interacts with surface materials along the way, often transforming between disaster types and forming a disaster chain with long-distance effects. The formation of this disaster chain significantly amplifies the scope and complexity of the disaster, and the harm it causes is difficult to estimate. The quantitative characterization of the roughness of the movement path of high-speed long-distance landslides can accurately determine its kinematic and dynamic parameters such as maximum movement speed, maximum movement distance, and maximum accumulation range, realize the analysis and prediction of the danger of high-speed long-distance landslide disasters, and determine the degree of disaster risk.

[0003] The existing method for characterizing the roughness of landslide movement paths is mainly a geometric statistical method. This method is based on the high-precision three-dimensional terrain data of the landslide path. By converting the geometric characteristics of the path surface into a quantifiable roughness index, the roughness of the movement path can be quantified. However, this method ignores the local changes in the surface of the movement path and has difficulty in accurately capturing subtle terrain changes, especially complex terrain and micro-topography. On the other hand, this method has limitations in its performance at different scales. The quantification result of the roughness will be affected by the scale of the selected area. It may ignore local details at a large scale and overemphasize tiny undulations at a small scale. In addition, the computational complexity is high when processing large-scale, high-resolution three-dimensional terrain data, and it is often impossible to efficiently and accurately quantify the roughness of the movement path. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the quantitative characterization method for the roughness of the high-speed long-distance landslide movement path provided by the present invention solves the problems of complex calculation of the existing quantitative characterization of the roughness of the high-speed long-distance landslide movement path, incomplete consideration of the terrain scale, and lack of detailed characterization of micro-topography features.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a quantitative characterization method for the roughness of a high-speed long-distance landslide movement path, comprising the following steps:

[0006] S1: Select the target landslide and its surrounding area, and obtain the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide, and the landslide boundary data of the target landslide;

[0007] S2: Obtain the elevation data of the target landslide movement path according to the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide and the landslide boundary data of the target landslide;

[0008] S3: Detrending the elevation data of the target landslide movement path to obtain the elevation data of the micro-topography of the landslide movement path;

[0009] S4: According to the elevation data of the micro-topography of the landslide movement path, the fractal dimension of the target landslide movement path after detrending is calculated based on the calculation method of the fractal dimension of the joint contour line;

[0010] S5: Based on the detrended fractal dimension of the target landslide movement path, the path roughness coefficient of the target landslide is calculated to complete the quantitative characterization of the roughness of the high-speed long-distance landslide movement path.

[0011] The beneficial effects of the present invention are as follows: the present invention quantitatively characterizes the roughness of the high-speed long-distance landslide movement path based on the path roughness coefficient. It only needs to count the average undulation height and average extension length of the microtopography on the landslide movement path, and based on the joint fractal model hL method of the joint contour line roughness characterization method in the field of rock mechanics, the fractal dimension of the high-speed long-distance landslide movement path after detrending and the path roughness coefficient of the landslide are calculated. The calculation process is simple and the application range is wide. The roughness of the high-speed long-distance landslide movement path can be revealed at different spatial scales, and the influence of the microtopography in the landslide movement process can be fully revealed, thereby providing a more comprehensive roughness characterization, and having a strong theoretical significance and practical application value.

[0012] Further: the specific steps of S1 are as follows:

[0013] S101: Select a target landslide and its surrounding area on a satellite image, and obtain remote sensing image data of the target landslide according to a set spatial pixel resolution, and obtain DEM digital elevation data of the target landslide according to a set elevation data sampling interval;

[0014] S102: In the satellite image, a polygon tool is used to mark the landslide boundary of the target landslide to obtain the landslide boundary data of the target landslide.

[0015] The beneficial effect of the above further scheme is: by obtaining the remote sensing image data and DEM digital elevation data of the target landslide, more comprehensive geological information can be provided to ensure sufficient details and accuracy, and the landslide boundary data of the target landslide can obtain the precise landslide area.

[0016] Further: the specific steps of S2 are as follows:

[0017] S201: Overlapping the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide, and the landslide boundary data of the target landslide, and keeping the coordinate system consistent, to obtain a map view of the target landslide;

[0018] S202: starting from the shear exit of the target landslide and extending along the main sliding direction of the landslide according to the map view of the target landslide, a longitudinal section line segment of the target landslide is obtained;

[0019] S203: translating the longitudinal profile line segment of the target landslide to the mountain area adjacent to the target landslide to approximately obtain the target landslide movement path line segment;

[0020] S204: obtaining a cross-sectional view of the target landslide movement path according to the target landslide movement path line segment;

[0021] S205: Obtaining elevation data of the target landslide movement path according to the cross-sectional view of the target landslide movement path.

[0022] The beneficial effect of the above further scheme is: based on the fact that the pre-landslide terrain of the landslide and the terrain characteristics of the adjacent mountains have certain similarities, the present invention can approximately obtain the elevation data of the target landslide movement path in the absence of pre-landslide terrain data, thereby approximately reflecting the elevation change characteristics of the actual landslide movement path.

[0023] Further: the specific steps of S3 are as follows:

[0024] S301: establishing a terrain trend function of the landslide movement path using a fitting function according to the elevation data of the target landslide movement path;

[0025] S302: according to the terrain trend function of the landslide movement path, obtaining the best fitting coefficient of the terrain trend function based on the least square method;

[0026] S303: Obtaining a fitted terrain trend function according to the best fitting coefficient;

[0027] S304: According to the fitted terrain trend function, the large-scale trend in the target landslide movement path is removed to obtain the elevation data of the micro-topography of the landslide movement path.

[0028] The beneficial effect of the above further scheme is that through detrending, large-scale terrain features that interfere with subsequent analysis can be removed, so that subsequent analysis can focus on small-scale local changes and improve the accuracy of quantitative characterization of roughness.

[0029] Further: the expression of the fractal dimension of the target landslide movement path after detrending in S4 is as follows:

[0030] D = log4 / log[2(1+costan -1 (2h / L))]

[0031] Where D is the fractal dimension of the target landslide movement path after detrending, h is the average height of the micro-topography on the landslide path, and L is the average extension length of the micro-topography on the landslide path.

[0032] The beneficial effect of the above further scheme is that by calculating the fractal dimension, the roughness of the landslide movement path can be quantified, the uncertainty thereof which changes with the observation scale can be eliminated, and the accuracy of the quantitative characterization of the roughness of the landslide movement path can be improved.

[0033] Further: the expression of the path roughness coefficient of the target landslide in S5 is as follows:

[0034] PRC=85.2671(D-1) 0.5679

[0035] Wherein, PRC is the path roughness coefficient of the target landslide, and D is the fractal dimension of the target landslide movement path after detrending.

[0036] The beneficial effect of the above further scheme is that by referring to the empirical formula between the joint roughness coefficient (JRC) and the fractal dimension, the path roughness coefficient of the target landslide calculated by the above formula can more intuitively characterize the roughness of the landslide movement path and reflect the local undulations and detailed characteristics on the landslide movement path. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of a quantitative characterization method for the roughness of a high-speed long-distance landslide movement path;

[0038] Figure 2 This is the longitudinal section of the landslide movement path. DETAILED DESCRIPTION

[0039] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0040] like Figure 1 As shown, a flow chart of a quantitative characterization method for the roughness of a high-speed long-distance landslide movement path includes the following steps:

[0041] S1: Select the target landslide and its surrounding area, and obtain the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide, and the landslide boundary data of the target landslide;

[0042] S2: Obtain the elevation data of the target landslide movement path according to the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide and the landslide boundary data of the target landslide;

[0043] S3: Detrending the elevation data of the target landslide movement path to obtain the elevation data of the micro-topography of the landslide movement path;

[0044] S4: According to the elevation data of the micro-topography of the landslide movement path, the fractal dimension of the target landslide movement path after detrending is calculated based on the calculation method of the fractal dimension of the joint contour line;

[0045] S5: Based on the detrended fractal dimension of the target landslide movement path, the path roughness coefficient of the target landslide is calculated to complete the quantitative characterization of the roughness of the high-speed long-distance landslide movement path.

[0046] Among them, the specific steps of S1 are as follows:

[0047] S101: Select a target landslide and its surrounding area on a satellite image, and obtain remote sensing image data of the target landslide according to a set spatial pixel resolution, and obtain DEM digital elevation data of the target landslide according to a set elevation data sampling interval;

[0048] S102: In the satellite image, a polygon tool is used to mark the landslide boundary of the target landslide to obtain the landslide boundary data of the target landslide.

[0049] In one embodiment of the present invention, the 91 Satellite Map Assistant platform can be used to download data. After entering the 91 Satellite Map Assistant, on the satellite image, the target landslide and its surrounding area are selected, and the required spatial pixel resolution of the image data is set, such as 5 meters or higher, to download the high-resolution remote sensing image data of the area; the sampling interval of the required elevation data is set, such as 8 meters or higher, to download the high-precision elevation DEM data of the area; the polygon tool is used in Google Earth to mark the landslide boundary of the target landslide, and the boundary of the target landslide is accurately drawn to ensure that the boundary range can accurately cover the main body of the landslide and the possible landslide area. After the drawing is completed, the landslide boundary image of the target landslide is obtained and saved in the kml file format.

[0050] Since the landslide and the adjacent mountain have the same or similar internal and external dynamic geological background, the pre-slide topography of the landslide and the topographic features of the adjacent mountain have certain similarities. Based on this similarity, the present invention proposes a method for approximately extracting the movement path of the landslide, that is, according to the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide and the landslide boundary data of the target landslide, the elevation data of the movement path of the target landslide is approximately obtained; the specific steps of S2 are as follows:

[0051] S201: Overlapping the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide, and the landslide boundary data of the target landslide, and keeping the coordinate system consistent, to obtain a map view of the target landslide;

[0052] S202: starting from the shear exit of the target landslide and extending along the main sliding direction of the landslide according to the map view of the target landslide, a longitudinal section line segment of the target landslide is obtained;

[0053] S203: translating the longitudinal profile line segment of the target landslide to the mountain area adjacent to the target landslide to approximately obtain the target landslide movement path line segment;

[0054] S204: obtaining a cross-sectional view of the target landslide movement path according to the target landslide movement path line segment;

[0055] S205: Obtaining elevation data of the target landslide movement path according to the cross-sectional view of the target landslide movement path.

[0056] In one embodiment of the present invention, the landslide boundary image of the target landslide can be imported into the 91 Satellite Map Assistant platform, and the target landslide boundary data is converted, and a suitable coordinate system is selected to convert it into a shp file format to ensure that the spatial information of the data is retained; the remote sensing image data (tiff format) of the target landslide, the DEM digital elevation data (tiff format) of the target landslide and the target landslide boundary data (shp format) are imported into ArcGIS to ensure that the coordinate systems of these data are consistent to avoid coordinate offset, and the spatial information of the landslide boundary is correctly mapped to the map view in ArcGIS to obtain the map view of the target landslide;

[0057] The movement path of a landslide usually refers to the terrain state below the shear outlet before the landslide occurs. The movement path of a landslide in the present invention is a two-dimensional movement path, that is, the elevation of the landslide changes along the main sliding direction. However, the reconstruction process of the terrain before the landslide is complex and may be affected by different geological actions. In order to circumvent this difficulty, the present invention proposes another method for approximately obtaining the movement path of a landslide. First, the 3DAnalyst toolbar is enabled in ArcGIS, and the "Insert Line" command is selected to draw a longitudinal section line segment extending from the shear outlet of the landslide along the main sliding direction of the landslide. Then, by translating the longitudinal section line segment to the adjacent mountain area of ​​the landslide, the target landslide path movement line segment is approximately obtained. The line segment is spatially close to the actual movement path of the landslide, and its elevation change can approximately reflect the elevation change characteristics of the real landslide movement path.

[0058] In the 3D Analyst toolbar, use the "Profile" command for the approximate target landslide path movement segment. ArcGIS will automatically generate an elevation change map along the path to show the elevation change of each position during the landslide movement process, and obtain a longitudinal profile of the landslide movement path, such as Figure 2 As shown; in the generated longitudinal profile window of the landslide movement path, select the "Export Data" option to save the elevation data along the path in .csv format to obtain the elevation data of the target landslide movement path.

[0059] In one embodiment of the present invention, the specific steps of S3 are as follows:

[0060] S301: Based on the elevation data of the target landslide movement path, a terrain trend function of the landslide movement path is established using a fitting function; wherein, the terrain trend function is established in this application to remove large-scale terrain fluctuations, such as slope and slope direction, which have a greater impact, leaving small-scale terrain features, such as micro-topography fluctuations or local irregular elevation fluctuations. The detrending object is the one-dimensional elevation data on the target landslide movement path, that is, the elevation change along the main landslide direction; therefore, the technician can select different detrending methods according to specific needs. This embodiment selects a linear fitting detrending method and a quadratic polynomial fitting detrending method for illustration, and the corresponding expressions of the terrain trend function are respectively:

[0061] T1(x)=a0+a1x

[0062] T2(x)=a2+a3x+a4x 2

[0063] Among them, T1(x) is a linear trend function, T2(x) is a quadratic polynomial trend function, a0, a1, a2, a3 and a4 are coefficients to be solved, and x is the distance along the main sliding direction of the landslide.

[0064] S302: According to the terrain trend function of the landslide movement path, the best fitting coefficient of the terrain trend function is obtained based on the least square method; the specific fitting method is as follows:

[0065] The linear fitting expression is as follows:

[0066]

[0067] The quadratic polynomial fitting expression is as follows:

[0068]

[0069] Among them, x i is the horizontal coordinate of the ith data in the DEM digital elevation data of the target landslide, Z(x i ) is the ordinate of the i-th data in the DEM digital elevation data of the target landslide;

[0070] By minimizing the sum of squares of errors between the fitting function and the observed data, the best fitting coefficients a0, a1, a2, a3 and a4 are solved.

[0071] S303: Obtaining a fitted terrain trend function according to the best fitting coefficient;

[0072] S304: According to the fitted terrain trend function, the large-scale trend in the target landslide movement path is removed to obtain the elevation data of the micro-topography of the landslide movement path, which can reflect the changes in small-scale terrain characteristics. The specific method of removing the large-scale trend is as follows:

[0073] The expression for linear fitting detrending is as follows:

[0074] E1(x)=Z(x)-T1(x)=Z(x)-(a0+a1x)

[0075] The expression for quadratic polynomial fitting detrending is as follows:

[0076] E2(x)=Z(x)-T2(x)=Z(x)-(a2+a3x+a4x 2 )

[0077] Among them, E1(x) is the micro-topography of the landslide movement path detrended by linear fitting, E2(x) is the micro-topography of the landslide movement path detrended by quadratic polynomial fitting, and Z(x) is the DEM digital elevation data of the target landslide movement path.

[0078] In one embodiment of the present invention, based on the joint roughness coefficient (JRC), the present invention proposes a "path roughness coefficient" (PRC). The joint roughness coefficient is a parameter used to quantitatively describe the geometric roughness of the rock joint surface. Based on the idea of ​​JRC characterizing the roughness of the joint contour line, the present invention proposes to use the path roughness coefficient to quantitatively characterize the roughness of the target landslide movement path, and the method is as follows:

[0079] According to the micro-topography of the landslide movement path, the fractal dimension of the target landslide movement path after detrending is calculated by referring to the calculation method of the fractal dimension of the joint contour line;

[0080] According to the fractal dimension of the target landslide movement path after detrending, the path roughness coefficient of the target landslide is calculated, and the quantitative characterization of the roughness of the high-speed long-distance landslide movement path is completed.

[0081] The expression of the fractal dimension of the target landslide movement path after detrending is as follows:

[0082] D = log4 / log[2(1+cos tan -1 (2h / L))]

[0083] Where D is the fractal dimension of the target landslide movement path after detrending, h is the average height of the micro-topography on the landslide path, and L is the average extension length of the micro-topography on the landslide path.

[0084] The expression of the path roughness coefficient of the target landslide is as follows:

[0085] PRC=85.2671(D-1) 0.5679

[0086] Among them, PRC is the path roughness coefficient of the target landslide, and D is the fractal dimension of the target landslide movement path after detrending. The path roughness coefficient is used to more intuitively characterize the roughness of the landslide movement path.

[0087] The beneficial effects of the present invention are as follows: the present invention quantitatively characterizes the roughness of the high-speed long-distance landslide movement path based on the path roughness coefficient. It only needs to count the average undulation height and average extension length of the micro-topography on the landslide movement path. Based on the joint fractal model hL method of the joint contour line roughness characterization method in the field of rock mechanics, the fractal dimension of the high-speed long-distance landslide movement path after detrending and the path roughness coefficient of the landslide are calculated. The calculation process is simple and the application range is wide. The roughness of the high-speed long-distance landslide movement path can be revealed at different spatial scales, and the influence of the micro-topography in the landslide movement process can be fully revealed, thereby providing a more comprehensive roughness characterization, and having a strong theoretical significance and practical application value.

Claims

1. A quantitative characterization method for the roughness of a high-speed long-distance landslide movement path, characterized in that: The following steps are involved: S1: Select the target landslide and its surrounding area, and obtain the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide, and the landslide boundary data of the target landslide; S2: Obtain the elevation data of the target landslide movement path according to the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide and the landslide boundary data of the target landslide; S3: Detrending the elevation data of the target landslide movement path to obtain the elevation data of the micro-topography of the landslide movement path; S4: According to the elevation data of the micro-topography of the landslide movement path, the fractal dimension of the target landslide movement path after detrending is calculated based on the calculation method of the fractal dimension of the joint contour line; S5: Based on the detrended fractal dimension of the target landslide movement path, the path roughness coefficient of the target landslide is calculated to complete the quantitative characterization of the roughness of the high-speed long-distance landslide movement path.

2. The quantitative characterization method for the roughness of a high-speed long-distance landslide movement path according to claim 1 is characterized in that: The specific steps of S1 are as follows: S101: Select a target landslide and its surrounding area on a satellite image, and obtain remote sensing image data of the target landslide according to a set spatial pixel resolution, and obtain DEM digital elevation data of the target landslide according to a set elevation data sampling interval; S102: In the satellite image, a polygon tool is used to mark the landslide boundary of the target landslide to obtain the landslide boundary data of the target landslide.

3. The quantitative characterization method for the roughness of a high-speed long-distance landslide movement path according to claim 1 is characterized in that: The specific steps of S2 are as follows: S201: Overlapping the remote sensing image data of the target landslide, the DEM digital elevation data of the target landslide, and the landslide boundary data of the target landslide, and keeping the coordinate system consistent, to obtain a map view of the target landslide; S202: starting from the shear exit of the target landslide and extending along the main sliding direction of the landslide according to the map view of the target landslide, a longitudinal section line segment of the target landslide is obtained; S203: translating the longitudinal profile line segment of the target landslide to the mountain area adjacent to the target landslide to approximately obtain the target landslide movement path line segment; S204: obtaining a cross-sectional view of the target landslide movement path according to the target landslide movement path line segment; S205: Obtaining elevation data of the target landslide movement path according to the cross-sectional view of the target landslide movement path.

4. The method for quantitatively characterizing the roughness of a high-speed long-distance landslide movement path according to claim 1 is characterized in that: The specific steps of S3 are as follows: S301: establishing a terrain trend function of the landslide movement path using a fitting function according to the elevation data of the target landslide movement path; S302: according to the terrain trend function of the landslide movement path, obtaining the best fitting coefficient of the terrain trend function based on the least square method; S303: Obtaining a fitted terrain trend function according to the best fitting coefficient; S304: According to the fitted terrain trend function, the large-scale trend in the target landslide movement path is removed to obtain the elevation data of the micro-topography of the landslide movement path.

5. The method for quantitatively characterizing the roughness of a high-speed long-distance landslide movement path according to claim 1 is characterized in that: The expression of the fractal dimension of the target landslide movement path after detrending in S4 is as follows: D=log4 / log[2(1+costan -1 (2h / L))] Where D is the fractal dimension of the target landslide movement path after detrending, h is the average height of the micro-topography on the landslide path, and L is the average extension length of the micro-topography on the landslide path.

6. The method for quantitatively characterizing the roughness of a high-speed long-distance landslide movement path according to claim 1 is characterized in that: The expression of the path roughness coefficient of the target landslide in S5 is as follows: PRC=85.2671(D-1) 0.5679 Wherein, PRC is the path roughness coefficient of the target landslide, and D is the fractal dimension of the target landslide movement path after detrending.

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