Water and soil conservation steep slope remote sensing supervision pattern spot interpretation method and system

Abstract

The invention discloses a water and soil conservation steep slope remote sensing supervision pattern spot interpretation method and system, and relates to the technical field of high-resolution satellite image processing, and the method comprises the following steps: S1, carrying out the multi-scale segmentation of a remote sensing image, and determining an optimal segmentation scale; s2, based on the brightness, the normalized vegetation index, the blue band mean value, the near-infrared band mean value, the ratio residential area index, the boundary index and the shape index, establishing a bare area extraction algorithm, and using the bare area extraction algorithm to remove interference ground objects in the remote sensing image segmented in the S1 to obtain a suspected disturbance pattern spot image; s3, DEM data are collected and preprocessed, gradient calculation is conducted through the preprocessed DEM data, and a gradient base map is obtained; and S4, performing image overlay analysis on the slope base map and the suspected disturbance pattern spots, and extracting the steep slope naked map spots in the target range. According to the method, the reclamation pattern spots in the gradient range can be extracted according to the reclamation prohibition range standards of steep slopes in different regions, and the extraction precision is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-resolution satellite image processing, and in particular to a method and system for interpreting remote sensing monitoring images of steep slopes for soil and water conservation. Background Art

[0002] Soil and water conservation monitoring is a statutory responsibility assigned to the water administration department by the Soil and Water Conservation Law, and the water and soil erosion and prevention situations must be announced to the public regularly based on the soil and water conservation monitoring results.

[0003] Farming on steep slopes is one of the important reasons for accelerating soil erosion. In order to strengthen the supervision of artificial soil and water loss and promote the extension of remote sensing supervision of soil and water conservation to agricultural and forestry development activities, it is urgent to carry out in-depth remote sensing supervision of steep slopes. The "Technical Regulations for Land Use Status Survey" divides the slope of cultivated land into five levels. Different levels of ground slope have different effects on cultivated land use. ≤2° Generally, there is no soil and water loss; 2°~6° may cause mild soil erosion, and attention should be paid to soil and water conservation; 6°~15° may cause moderate soil and water loss, and measures such as building terraces and contour planting should be taken to strengthen soil and water conservation; 15°~25° soil and water loss is serious, and comprehensive measures such as engineering and biology must be taken to prevent and control soil and water loss; greater than 25° is the reclamation restriction slope stipulated in the "Soil and Water Conservation Law", that is, it is prohibited to cultivate crops on steep slopes above 25 degrees. When planting economic forests on steep slopes above 25 degrees, tree species should be scientifically selected, the scale should be reasonably determined, and soil and water conservation measures should be taken to prevent soil and water loss.

[0004] In the face of the demand for soil and water conservation supervision, it is urgent to explore the interpretation method of steep slope remote sensing supervision. However, the existing methods for extracting suspected spots are mostly to extract all suspected spots in a small area through manual visual interpretation. There is no technical solution for automatically extracting disturbed spots suspected of agricultural and forestry development activities based on the terrain slope range, and it is impossible to distinguish and extract agricultural and forestry development activities in a large area according to the relevant laws and regulations of different regions. Therefore, how to use high-resolution remote sensing images to accurately and widely extract disturbed spots suspected of agricultural and forestry development activities within the target slope range is a key issue faced in further strengthening the supervision of man-made soil and water loss and promoting the extension of soil and water conservation remote sensing supervision to agricultural and forestry development activities. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for interpreting remote sensing monitoring patches on steep slopes for soil and water conservation. The method can extract target patches after removing different types of interfering objects, and extract suspected reclamation patches within the prohibited slope range of steep slopes through overlay analysis. The method is suitable for extracting suspected disturbance patches in a large area.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for interpreting remote sensing monitoring images of steep slopes for soil and water conservation, comprising the following steps:

[0008] S1. Perform multi-scale segmentation on remote sensing images and determine the optimal segmentation scale;

[0009] S2, establishing a bare land extraction algorithm based on brightness, normalized vegetation index, blue band mean, near infrared band mean, ratio residential land index, boundary index, and shape index, and using the bare land extraction algorithm to remove interfering objects in the remote sensing image segmented in S1 to obtain a suspected disturbance spot image;

[0010] S3, collecting DEM data and preprocessing it, using the preprocessed DEM data to calculate the slope and obtain a slope base map;

[0011] S4. Overlay and analyze the slope base map and the suspected disturbance patch image to extract the bare ground patches on the steep slope within the target range.

[0012] Furthermore, in S2, the bare land extraction algorithm is used to remove the interfering objects in the remote sensing image segmented in S1 to obtain a suspected disturbance spot image, which specifically includes the following steps:

[0013] S201, using a brightness algorithm to define the maximum range of the bare ground, with the threshold range set to: Brightness ≥ im_Brightness, where im_Brightness is the brightness threshold for effectively distinguishing the bare ground from other objects;

[0014] S202, using the normalized vegetation index to remove the confusing vegetation objects in the remote sensing image obtained in S201;

[0015] The calculation formula of the normalized vegetation index is:

[0016] NDVI=(NIR-Red) / (NIR+Red),

[0017] In the formula, NIR represents the near infrared band, and Red represents the red light band;

[0018] The threshold range is set as: NDVI ≥ im_NDVI, where im_NDVI is the first critical value for effectively distinguishing green vegetation from other objects, and the first critical value varies from image to image;

[0019] S203, for the remote sensing image obtained in S202, gray-white buildings and part of the sandy land are eliminated by using the blue light band mean, the near infrared band mean, and the ratio residential area index, wherein the calculation formulas for the blue light band mean and the near infrared band mean are:

[0020]

[0021] In the formula, represents the average brightness value of the ground feature spot v on the layer L; n represents the total number of pixels of the ground feature spot v; C Li Represents the brightness value of the i-th pixel in the ground feature patch v on the layer L;

[0022] The threshold range is set as: Mean Fu_B1≤im_B1, Mean Fu_B4≥im_B4, where im_B1 and im_B4 are the second critical values ​​for effectively distinguishing sand and impermeable layer from other landforms, respectively, and Fu_B1 and Fu_B4 refer to the blue band and near-infrared band of the fused data, respectively; Mean Fu_B1 represents the mean of the blue band, and Mean Fu_B4 represents the mean of the near-infrared band;

[0023] The calculation formula of the ratio settlement index is: RRI = Band1 / Band4

[0024] In the formula, Band1 represents the blue light band, and Band4 represents the near-infrared band;

[0025] When the image obtained by fusion of multispectral and panchromatic data is selected as the basic data, the blue light band and near infrared band of the fused image are selected to construct the simulated RRI. The calculation formula of the simulated RRI is:

[0026] Simulated RRI = Fu_Band1 / Fu_Band4

[0027] In the formula, Fu_Band1 represents the blue light band of the fused image, and Fu_Band4 represents the near infrared band of the fused image;

[0028] The threshold range is set as: RRI ≥ im_RRI, where im_RRI is the third critical value that effectively distinguishes the impermeable layer from other land features;

[0029] S204, using the shape index algorithm to remove the confused sand from the image after the elimination process in step S203, to obtain a suspected disturbance spot image; the threshold range is set as: Shape index ≥ im_Shape index, where im_Shapeindex is the fourth critical value that effectively distinguishes sand, buildings and other objects.

[0030] Furthermore, the preprocessing in S3 specifically includes: removing noise, filling missing values, and performing interpolation.

[0031] Furthermore, in S3, the slope is calculated using the pre-processed DEM data to obtain a slope base map, which specifically includes the following steps:

[0032] The slope is determined by the rate of change of the surface from the center pixel in the horizontal and vertical directions; the basic algorithm for calculating the slope is as follows:

[0033] In the formula, x and y are the plane coordinates (x, y) of the point corresponding to the pixel on the ground, and z is the height of the point;

[0034] The rate of change of pixel e in the x direction is calculated by the following algorithm:

[0035]

[0036] The rate of change of pixel e in the y direction is calculated as follows:

[0037]

[0038] Where, a, b, ..., i are the elevations of each grid; wght1 and wght2 are weighted counts of valid pixels;

[0039] After the calculation is completed, the slope base map is generated.

[0040] Furthermore, the S4, superimposing and analyzing the slope base map and the suspected disturbance patch image to extract the steep slope bare map patches within the target range, specifically comprises the following steps:

[0041] Import the slope base map and the spot base map into the GIS software and ensure that they have the correct coordinate system; use the overlay analysis tool "Intersect" to specify the slope base map and the spot extraction result base map as input elements respectively, perform overlay operations, and generate overlay results; the overlay results contain a combination of slope and spot information, which is used to clarify which spots are in a specific slope area;

[0042] The slope threshold is set as needed, and the patches within different slope ranges are screened to ultimately determine the distribution of bare map patches on steep slopes within the target slope range.

[0043] The present invention also provides a soil and water conservation steep slope remote sensing supervision map interpretation system, which is used to execute the soil and water conservation steep slope remote sensing supervision map interpretation method, comprising:

[0044] The segmentation module is used to perform multi-scale segmentation on remote sensing images and determine the optimal segmentation scale;

[0045] A removal module is used to establish a bare land extraction algorithm based on brightness, normalized vegetation index, blue band mean, near infrared band mean, ratio residential land index, boundary index, and shape index, and use the bare land extraction algorithm to remove interfering objects in the segmented remote sensing image to obtain a suspected disturbance spot image;

[0046] The slope base map acquisition module is used to collect and preprocess DEM data, and use the preprocessed DEM data to calculate the slope to obtain the slope base map;

[0047] The overlay analysis module is used to perform overlay analysis on the slope base map and the suspected disturbance patch image to extract the bare map patches on the steep slope within the target range.

[0048] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: The method and system for interpreting remote sensing supervision spots on steep slopes of soil and water conservation provided by the present invention adopt multi-scale segmentation for remote sensing images, determine the optimal segmentation scale, and refer to the commonly used algorithm for bare land extraction to remove different types of interfering objects and extract target spots. Compared with the traditional manual interpretation and extraction method, the present invention has the characteristics of high extraction accuracy and high speed, and is suitable for the extraction of suspected disturbance spots in a large area. In addition, the present invention uses the DEM produced by stereo mapping remote sensing data to calculate the slope, and extracts the suspected reclamation spots within the prohibited slope range of steep slopes by overlaying and analyzing with the suspected disturbance spot images. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0050] Figure 1 It is a flow chart of a method for interpreting remote sensing monitoring images of steep slopes for soil and water conservation according to an embodiment of the present invention;

[0051] Figure 2 This is a flow chart of extracting a suspected disturbance spot image according to an embodiment of the present invention;

[0052] Figure 3 Schematic diagram of the slope calculation grid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] The purpose of the present invention is to provide a method and system for interpreting remote sensing monitoring patches on steep slopes for soil and water conservation. The method can extract target patches after removing different types of interfering objects, and extract suspected reclamation patches within the prohibited slope range of steep slopes through overlay analysis. The method is suitable for extracting suspected disturbance patches in a large area.

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] like Figure 1-3 As shown, a method for interpreting remote sensing monitoring images of steep slopes of soil and water conservation provided by an embodiment of the present invention includes the following steps:

[0057] S1. Perform multi-scale segmentation on remote sensing images and determine the optimal segmentation scale;

[0058] S2, establishing a bare land extraction algorithm based on brightness, normalized vegetation index, blue band mean, near infrared band mean, ratio residential land index, boundary index, and shape index, and using the bare land extraction algorithm to remove interfering objects in the remote sensing image segmented in S1 to obtain a suspected disturbance spot image;

[0059] S3, collecting DEM data and preprocessing it, using the preprocessed DEM data to calculate the slope and obtain a slope base map;

[0060] S4. Overlay and analyze the slope base map and the suspected disturbance patch image to extract the bare ground patches on the steep slope within the target range.

[0061] In this embodiment, S1, performing multi-scale segmentation on the remote sensing image to determine the optimal segmentation scale, specifically includes:

[0062] The purpose of image segmentation is to identify the target objects. Therefore, different target objects have different segmentation scales and parameters. In the image segmentation process, the optimal segmentation scale should make the segmented object internally homogeneous, with complete and clear boundaries, so as to improve the efficiency of classification and the accuracy of information extraction. Specifically, by changing multiple segmentation scales, visually analyzing the degree of fit between the segmented object and the actual object, the multi-scale segmentation results can be evaluated to determine the optimal segmentation scale. The near-infrared band, red band, green band and blue band participate in the segmentation in a ratio of 1:1:1:1, represented by 4, 3, 2 and 1 respectively.

[0063] In this embodiment, the bare ground has high reflectivity, low vegetation coverage, uniform texture, and is easily mixed with sand, impermeable layer (building), vegetation, etc. Brightness (Brighmess), Normalized Difference Vegetation Index (NDVI), Blue Band Mean (Mean Fu_B1), Near Infrared Band Mean (Mean Fu_B4), Ratio Resident-area Index (RRI)\Border Index (Shape index), Shape Index (Border index) are used to eliminate other objects confused with the bare ground. The summary table of bare ground extraction algorithms is shown in Table 1.

[0064] Table 1 Summary of bare ground extraction algorithms

[0065]

[0066] In this embodiment, in S2, the bare land extraction algorithm is used to remove the interfering objects in the remote sensing image segmented in S1 to obtain a suspected disturbed spot image, which specifically includes the following steps:

[0067] S201. Use a brightness algorithm to define the maximum range of the bare ground. The brightness algorithm can extract the maximum range of the bare ground, which contains a variety of highly reflective land types. The threshold range is set to: Brightness ≥ im_Brightness, where im_Brightness is the brightness threshold that effectively distinguishes the bare ground from other land objects. The brightness threshold varies from image to image.

[0068] S202, using the Normalized Difference Vegetation Index (NDVI) to remove the confusing vegetation objects in the remote sensing image obtained in S201;

[0069] The calculation formula of the normalized vegetation index is:

[0070] NDVI=(NIR-Red) / (NIR+Red),

[0071] In the formula, NIR represents the near infrared band, and Red represents the red light band;

[0072] The threshold range is set as: NDVI ≥ im_NDVI, where im_NDVI is the first critical value for effectively distinguishing green vegetation from other objects. The first critical value varies depending on the image.

[0073] S203, for the remote sensing image obtained in S202, the gray-white buildings and part of the sandy land are eliminated using the blue band mean (Mean Fu_B1), the near infrared band mean (Mean Fu_B4), and the ratio residential area index (RRI), and the calculation formulas of the blue band mean and the near infrared band mean are:

[0074]

[0075] In the formula, represents the average brightness value of the ground feature spot v on the layer L; n represents the total number of pixels of the ground feature spot v; C Li Represents the brightness value of the i-th pixel in the ground feature patch v on the layer L;

[0076] The threshold range is set as: Mean Fu_B1≤im_B1, Mean Fu_B4≥im_B4, where im_B1 and im_B4 are the second critical values ​​for effectively distinguishing sand and impermeable layer from other landforms, respectively, and the second critical value varies depending on the image; FuB1 and Fu_B4 refer to the blue light band and near-infrared band of the fused data, respectively; MeanFu_B1 represents the mean of the blue band, and MeanFu_B4 represents the mean of the near-infrared band.

[0077] Since the spectral characteristics of the impermeable layer in the blue light band (Band 1) and the near-infrared band (Band 4) have a large contrast, the difference between the impermeable layer and other landforms can be well highlighted. Therefore, the ratio residential index (RRI) value is large, and the impermeable layer can be eliminated through the ratio residential index.

[0078] The calculation formula of the ratio settlement index (RRI) is: RRI = Band1 / Band4

[0079] In the formula, Band1 represents the blue light band, and Band4 represents the near-infrared band;

[0080] When the image after fusion of multispectral and panchromatic data is used as the basic data, the blue band and near-infrared band of the fused image are used to construct the simulated RRI. The simulated RRI is consistent with the spectral curve of the ground object reflected by the RRI, and the former has higher spatial resolution and extraction accuracy. The calculation formula of the simulated RRI is:

[0081] Simulated RRI = Fu_Band1 / Fu_Band4

[0082] In the formula, Fu_Band1 represents the blue light band of the fused image, and Fu_Band4 represents the near infrared band of the fused image;

[0083] The threshold range is set as: RRI ≥ im_RRI, where im_RRI is the third critical value that effectively distinguishes the impermeable layer from other landforms. The third critical value varies depending on the image.

[0084] S204. For the image after the elimination process in step S203, the shape index algorithm is used to eliminate the confused sand, and obtain the suspected disturbance spot image; the threshold range is set as: Shapeindex≥im_Shape index, where im_Shapeindex is the fourth critical value for effectively distinguishing sand, buildings and other objects, and the fourth critical value varies from image to image.

[0085] In this embodiment, the preprocessing in S3 specifically includes: removing noise, filling missing values, interpolation and other operations to ensure data quality and accuracy. If the projection of the original DEM is inconsistent with the requirements of subsequent analysis, the projection conversion can be performed through the "Projection and Transformation" tool. Use the "3D Analysis Tools" and "Raster Surface" in the GIS software data analysis toolbox; select the "Slope" tool in the "Raster Surface" tool set; select the input DEM data, and set the path and file name of the output slope base map to generate elevation raster data.

[0086] In this embodiment, in S3, the slope is calculated using the pre-processed DEM data to obtain a slope base map, which specifically includes the following steps:

[0087] like Figure 3 As shown, the slope depends on the rate of change (increment) of the surface from the center pixel (e) in the horizontal direction (dz / dx) and the vertical direction (dz / dy); the basic algorithm for calculating the slope is as follows:

[0088]

[0089] In the formula, x and y are the plane coordinates (x, y) of the point corresponding to the pixel on the ground, and z is the height of the point;

[0090] The rate of change of pixel e in the x direction is calculated by the following algorithm:

[0091]

[0092] The rate of change of pixel e in the y direction is calculated using the following algorithm:

[0093]

[0094] Where, a, b, ..., i are the elevations of each grid; wght1 and wght2 are weighted counts of valid pixels;

[0095] After the calculation is completed, a slope base map is generated.

[0096] In this embodiment, the S4, superimposing and analyzing the slope base map and the suspected disturbance spot image to extract the steep slope bare map spots within the target range, specifically includes the following steps:

[0097] Import the slope base map and the patch base map into the GIS software and ensure that they have the correct coordinate system; use the overlay analysis tool "Intersect" to specify the slope base map and the patch extraction result base map as input features respectively, perform the overlay operation, and generate the overlay result; the overlay result will contain a combination of slope and patch information, so that you can intuitively understand which patches are in a specific slope area, set the slope threshold as needed, screen the patches within different slope ranges, and finally determine the distribution of bare map patches on steep slopes within the target slope range, that is, determine the distribution of bare map patches on steep slopes within the target slope range.

[0098] Example 2

[0099] The embodiment of the present invention provides a remote sensing monitoring image interpretation system for steep slope land of soil and water conservation, which is used to execute the remote sensing monitoring image interpretation method for steep slope land of soil and water conservation described in Example 1, including:

[0100] The segmentation module is used to perform multi-scale segmentation and multi-threshold segmentation on remote sensing images and determine the optimal segmentation scale;

[0101] A removal module is used to establish a bare land extraction algorithm based on brightness, normalized vegetation index, blue band mean, near infrared band mean, ratio residential land index, boundary index, and shape index, and use the bare land extraction algorithm to remove interfering objects in the segmented remote sensing image to obtain a suspected disturbance spot image;

[0102] The slope base map acquisition module is used to collect and preprocess DEM data, and use the preprocessed DEM data to calculate the slope to obtain the slope base map;

[0103] The overlay analysis module is used to perform overlay analysis on the slope base map and the suspected disturbance patch image to extract the bare map patches on the steep slope within the target range.

[0104] In summary, the present invention provides a method and system for interpreting remote sensing monitoring patches on steep slopes for soil and water conservation. With the help of DEM data, the remote sensing image data of the current period and the previous period are fitted together, and the suspected reclamation patches within the target slope range are extracted based on the slope ranges of different steep slopes. The present invention adopts multivariate data comprehensive analysis and multi-scale multi-algorithm collaborative algorithms to construct a method for extracting suspected reclamation patches within the prohibited reclamation slope range of steep slopes based on high-resolution remote sensing images and high-precision DEM data. It innovates the extraction method of disturbed patches on steep slopes, and can extract suspected reclamation patches within the slope range according to the prohibited reclamation range standards for steep slopes in different regions, and the extraction accuracy is greatly improved, providing strong technical support for soil and water conservation monitoring work.

[0105] The remaining technical features in this embodiment can be flexibly selected by those skilled in the art according to actual conditions to meet different specific practical needs. However, it is obvious to those skilled in the art that it is not necessary to adopt these specific details to implement the present invention. In other examples, in order to avoid confusing the present invention, the well-known components, structures or parts are not specifically described, which are all within the technical protection scope defined by the technical solution claimed for protection in the claims of the present invention.

[0106] Modifications and changes made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the scope of protection of the claims attached to the present invention. In the above description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details are not necessary to practice the present invention. In other examples, in order to avoid confusing the present invention, well-known technologies, such as specific construction details, operating conditions and other technical conditions, are not specifically described.

[0107] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for interpreting remote sensing monitoring images of steep slopes for soil and water conservation, characterized in that: The following steps are involved: S1. Perform multi-scale segmentation on remote sensing images and determine the optimal segmentation scale; S2, establishing a bare land extraction algorithm based on brightness, normalized vegetation index, blue band mean, near infrared band mean, ratio residential land index, boundary index, and shape index, and using the bare land extraction algorithm to remove interfering objects in the remote sensing image segmented in S1 to obtain a suspected disturbance spot image; S3, collecting DEM data and preprocessing it, using the preprocessed DEM data to calculate the slope and obtain a slope base map; S4. Overlay and analyze the slope base map and the suspected disturbance patch image to extract the bare ground patches on the steep slope within the target range.

2. The method for interpreting remote sensing monitoring images of steep slopes for soil and water conservation according to claim 1 is characterized in that: In S2, the bare land extraction algorithm is used to remove the interfering objects in the remote sensing image segmented in S1 to obtain a suspected disturbed spot image, which specifically includes the following steps: S201, using a brightness algorithm to define the maximum range of the bare ground, with the threshold range set to: Brightness ≥ im_Brightness, where im_Brightness is the brightness threshold for effectively distinguishing the bare ground from other objects; S202, using the normalized vegetation index to remove the confusing vegetation objects in the remote sensing image obtained in S201; The calculation formula of the normalized vegetation index is: NDVI=(NIR-Red) / (NIR+Red), In the formula, NIR represents the near infrared band, and Red represents the red light band; The threshold range is set as: NDVI ≥ im_NDVI, where im_NDVI is the first critical value for effectively distinguishing green vegetation from other objects, and the first critical value varies from image to image; S203, for the remote sensing image obtained in S202, gray-white buildings and part of the sandy land are eliminated by using the blue light band mean, the near infrared band mean, and the ratio residential area index, wherein the calculation formulas for the blue light band mean and the near infrared band mean are: In the formula, represents the average brightness value of the ground feature spot v on the layer L; n represents the total number of pixels of the ground feature spot v; C Li Represents the brightness value of the i-th pixel in the ground feature patch v on the layer L; The threshold range is set as: MeanFu_B1≤im_B1, Mean Fu_B4≥im_B4, where im_BI and im_B4 are the second critical values ​​for effectively distinguishing sand and impermeable layer from other landforms, respectively, Fu_B1 and Fu_B4 refer to the blue band and near-infrared band of the fused data, respectively; MeanFu_B1 represents the mean of the blue band, and Mean Fu_B4 represents the mean of the near-infrared band; The calculation formula of the ratio settlement index is: RRI = Band1 / Band4 In the formula, Band1 represents the blue light band, and Band4 represents the near-infrared band; When the image obtained by fusion of multispectral and panchromatic data is selected as the basic data, the blue light band and near infrared band of the fused image are selected to construct the simulated RRI. The calculation formula of the simulated RRI is: Simulated RRI = Fu_Band1 / Fu_Band4 In the formula, Fu_Band1 represents the blue light band of the fused image, and Fu_Band4 represents the near infrared band of the fused image; The threshold range is set as: RRI ≥ im_RRI, where im_RRI is the third critical value that effectively distinguishes the impermeable layer from other land features; S204, using the shape index algorithm to remove the confused sand from the image after the elimination process in step S203, to obtain a suspected disturbance spot image; the threshold range is set as: Shape index ≥ im_Shape index, where im_Shape index is the fourth critical value that effectively distinguishes sand, buildings and other objects.

3. The method for interpreting remote sensing monitoring patterns of steep slopes for soil and water conservation according to claim 1 is characterized in that: The preprocessing in S3 specifically includes: removing noise, filling missing values, and performing interpolation.

4. The method for interpreting remote sensing monitoring patterns of steep slopes for soil and water conservation according to claim 1 is characterized in that: In S3, the slope is calculated using the pre-processed DEM data to obtain a slope base map, which specifically includes the following steps: The slope is determined by the rate of change of the surface from the center pixel in the horizontal and vertical directions; the basic algorithm for calculating the slope is as follows: In the formula, x and y are the plane coordinates (x, y) of the point corresponding to the pixel on the ground, and z is the height of the point; The rate of change of pixel e in the x direction is calculated by the following formula: The rate of change of pixel e in the y direction is calculated by the following formula: Where, a, b, ..., i are the elevations of each grid; wght1 and wght2 are weighted counts of valid pixels; After the calculation is completed, the slope base map is generated.

5. The method for interpreting remote sensing monitoring images of steep slopes for soil and water conservation according to claim 1 is characterized in that: S4, superimposing and analyzing the slope base map and the suspected disturbance patch image to extract the steep slope bare map patches within the target range, specifically comprises the following steps: Import the slope base map and the spot base map into the GIS software and ensure that they have the correct coordinate system; use the overlay analysis tool "Intersect" to specify the slope base map and the spot extraction result base map as input elements respectively, perform overlay operations, and generate overlay results; the overlay results contain a combination of slope and spot information, which is used to clarify which spots are in a specific slope area; The slope threshold is set as needed, and the patches within different slope ranges are screened to ultimately determine the distribution of bare map patches on steep slopes within the target slope range.

6. A remote sensing monitoring image interpretation system for steep slope land of soil and water conservation, used to execute the remote sensing monitoring image interpretation method for steep slope land of soil and water conservation as claimed in any one of claims 1 to 5, characterized in that: include: The segmentation module is used to perform multi-scale segmentation on remote sensing images and determine the optimal segmentation scale; A removal module is used to establish a bare land extraction algorithm based on brightness, normalized vegetation index, blue band mean, near infrared band mean, ratio residential land index, boundary index, and shape index, and use the bare land extraction algorithm to remove interfering objects in the remote sensing image segmented in S1 to obtain a suspected disturbance spot image; The slope base map acquisition module is used to collect and preprocess DEM data, and use the preprocessed DEM data to calculate the slope to obtain the slope base map; The overlay analysis module is used to perform overlay analysis on the slope base map and the suspected disturbance patch image to extract the bare map patches on the steep slope within the target range.

Images