Water and soil loss pattern spot comprehensive drawing method based on spatial aggregation degree analysis

Abstract

The embodiment of the invention discloses a water and soil loss pattern spot comprehensive drawing method based on spatial aggregation degree analysis, and relates to the field of geographic information technology and image data processing. The method comprises the steps that water and soil loss pattern spot vector data of a target area are acquired, rasterization processing is carried out on the water and soil loss pattern spot vector data to obtain raster data corresponding to each raster unit, and the raster data corresponding to each raster unit at least comprises soil erosion information, land utilization type information and DEM information; calculating an aggregation degree characterization parameter corresponding to each grid unit based on the grid data; determining spatial aggregation characteristics of the water and soil loss pattern spots of the target area according to the aggregation degree characterization parameters, and drawing an aggregation degree analysis image of the water and soil loss pattern spots according to the spatial aggregation characteristics; and performing hierarchical expression and layer creation on the water and soil loss pattern spots by using the aggregation degree analysis image of the water and soil loss pattern spots as a reference basis, and synthesizing a final water and soil loss pattern spot comprehensive graph.

Description

Technical Field

[0001] The present application relates to the fields of geographic information technology and image data processing, and in particular to a method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis. Background Art

[0002] In recent years, there have been many studies on the use of remote sensing technology to monitor the current status of soil erosion, invert the loss area and analyze the causes, zoning and prevention planning of soil erosion, assess the potential and capacity of soil and water conservation, and dynamic monitoring of soil erosion, and certain progress has been made in both theoretical and technical methods.

[0003] Currently, the main types of soil and water erosion zoning results based on remote sensing surveys include raster data, discrete data, and vector data. However, raster-based soil and water erosion zoning results cannot be spatially overlaid or queried in spatial analysis, resulting in fuzzy spatial relationships, low spatial data integration, and inability to serve as data for geographic information system construction. Discrete soil and water erosion data, on the other hand, cannot reflect detailed patch data on soil and water erosion, hindering soil and water erosion control, management, and monitoring. While vector data offers the advantages of vector data in data storage, editing, spatial analysis, data conversion, retrieval, and management, it also suffers from the following shortcomings: First, based on national survey data, patch boundaries in soil and water erosion vector data are often jagged, irregular, and relatively inaccurate, making them unsuitable for practical control and monitoring. Second, vector data for soil and water erosion typically requires a large amount of data, which increases the complexity and management costs of control plan analysis.

[0004] Based on this, it is necessary to study a comprehensive mapping method for soil and water loss patches based on spatial aggregation analysis to solve the problems of irregular boundaries, poor accuracy, and excessive data volume in existing soil and water loss vector data, as well as the problem of fuzzy spatial relationships in raster data. Summary of the Invention

[0005] To solve at least some of the above problems, an embodiment of this specification provides a method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis, the method comprising:

[0006] Obtaining soil and water loss spot vector data of the target area, and performing rasterization processing on the soil and water loss spot vector data to obtain raster data corresponding to each raster unit, wherein the raster data corresponding to each raster unit includes at least soil erosion information, land use type information, and DEM information;

[0007] Calculating the aggregation characterization parameter of the soil and water loss patch corresponding to each grid unit based on the grid data;

[0008] Determining spatial aggregation characteristics of the soil and water erosion patches in the target area according to the aggregation characterization parameters, and drawing an aggregation analysis image of the soil and water erosion patches according to the spatial aggregation characteristics, wherein the spatial aggregation characteristics are used to reflect the spatial distribution characteristics and boundary position characteristics of the soil and water erosion patches in one or more sub-areas contained in the target area;

[0009] Using the aggregation analysis image of the soil and water loss patches as a reference, the soil and water loss patches are hierarchically expressed and layers are created, and a final soil and water loss patch comprehensive map is synthesized.

[0010] In some embodiments, the rasterizing of the soil and water loss map vector data includes:

[0011] Converting the boundary coordinates of the soil and water loss map vector data into grid coordinates;

[0012] The soil and water loss map is divided into corresponding grid units according to the grid coordinates, and attribute information of the soil and water loss map is assigned to each grid unit to form rasterized soil and water loss map data, wherein the attribute information of the soil and water loss map includes at least soil erosion information, land use type information and DEM information.

[0013] In some embodiments, the calculating, based on the grid data, the aggregation characterization parameter of the soil and water loss patch corresponding to each grid unit includes:

[0014] Calculating the similarity of attribute information of each grid cell and the soil erosion patches corresponding to adjacent grid cells based on the grid data, wherein the similarity is calculated using any one of cosine similarity, Euclidean distance, and Manhattan distance;

[0015] The aggregation characterization parameter of the soil and water loss patch corresponding to each grid unit is determined according to the similarity.

[0016] In some embodiments, determining the aggregation characterization parameter of the soil and water loss patch corresponding to each grid unit according to the similarity includes:

[0017] For each target grid cell, the similarities between each adjacent grid cell and the target grid cell are arranged in a specified order to obtain a similarity sequence, and the similarity sequence is used as the aggregation characterization parameter of the soil and water loss map corresponding to the target grid cell.

[0018] In some embodiments, determining the spatial aggregation characteristics of the soil and water loss patches in the target area according to the aggregation characterization parameter includes:

[0019] Based on the aggregation characterization parameter, the adjacent grid cells and the target grid cell whose similarity is greater than a first preset threshold are divided into one cluster, and the clusters having the soil and water loss patches intersecting within a preset range are merged into a larger cluster until there are no clusters that can be merged, thereby obtaining one or more soil and water loss patch clusters;

[0020] Each soil and water loss patch cluster is regarded as a sub-area of the target area, and the spatial clustering characteristics of the soil and water loss patches of each sub-area contained in the target area are determined based on the soil and water loss patch clusters, wherein the spatial clustering characteristics include spatial distribution characteristics and boundary position characteristics, the spatial distribution characteristics include the size of each cluster and the distribution density of the soil and water loss patches within each cluster, and the boundary position characteristics include the boundary position coordinates corresponding to each cluster.

[0021] In some embodiments, determining the spatial clustering characteristics of the soil and water loss patches of each sub-region included in the target region based on the soil and water loss patch clusters includes:

[0022] Using ArcGIS, key grid cells at the boundary of each soil and water loss patch cluster are extracted as boundary points, and the boundary position coordinates of each soil and water loss patch cluster are obtained based on the position coordinates of the boundary points.

[0023] For each soil and water loss patch cluster, the size of the corresponding cluster is obtained by counting the number of all grid cells contained in the corresponding boundary position coordinate range, and the distribution density of the soil and water loss patches in each cluster is obtained by calculating the ratio of the number of grid cells corresponding to the soil and water loss patches within the boundary position coordinate range to the size of the cluster to which they belong.

[0024] In some embodiments, drawing an aggregation analysis image of soil and water loss patches according to the spatial aggregation characteristics includes:

[0025] For each of the soil and water loss patch clusters, a corresponding boundary line is determined based on the size of the corresponding cluster, the boundary position coordinates, and the distribution density of the corresponding soil and water loss patch, to obtain an aggregation analysis image of the soil and water loss patch.

[0026] In some embodiments, for each of the soil and water loss patches clusters, determining a corresponding boundary line based on the size of the corresponding cluster, the boundary position coordinates, and the distribution density of the corresponding soil and water loss patches includes:

[0027] Obtaining the position coordinates of all boundary points in the target cluster, and connecting all boundary points in the target cluster by smooth curves and / or straight lines to obtain an initial boundary line;

[0028] Searching for target adjacent boundary points whose distance is greater than a second preset threshold, and optimizing the initial boundary line at the target adjacent boundary point based on the size of the cluster and the distribution density of the soil and water loss patches to obtain a target boundary line.

[0029] In some embodiments, connecting all boundary points in the target cluster by smooth curves and / or straight lines comprises:

[0030] For a plurality of continuous boundary points that are in a linear position relationship and the distance between two adjacent boundary points is less than a third preset threshold, they are connected by a straight line; otherwise, they are connected by a smooth curve;

[0031] The target adjacent boundary points include a first boundary point and a second boundary point of a first type, searching for target adjacent boundary points whose distance is greater than a second preset threshold, and optimizing the initial boundary line at the target adjacent boundary points based on the size of the cluster and the distribution density of the soil and water loss patches, including:

[0032] Acquire a third boundary point located between the first boundary point and the second boundary point, wherein a first distance relative to the first boundary point and a second distance relative to the second boundary point satisfy a preset condition, wherein the third boundary point belongs to the second type;

[0033] The first boundary point, the third boundary point, and the second boundary point are sequentially connected based on a Bezier curve, and the initial boundary line at the target adjacent boundary point is replaced according to the connection result; wherein the curvature of the Bezier curve is negatively correlated with the size of the cluster and positively correlated with the distribution density of the soil and water loss patch.

[0034] In some embodiments, using the aggregation analysis image of the soil and water loss patches as a reference, hierarchically expressing the soil and water loss patches and creating layers, and synthesizing a final soil and water loss patch comprehensive map, includes:

[0035] The corresponding comprehensive index is calculated based on the size of each soil and water loss patch cluster and the distribution density of its corresponding soil and water loss patch;

[0036] Classifying the soil and water loss patch clusters into different levels according to the comprehensive index, and creating a corresponding layer for each level of soil and water loss patch;

[0037] The soil and water loss map layers of different levels are superimposed to obtain the soil and water loss map comprehensive map.

[0038] The beneficial effects of the comprehensive mapping method for soil and water loss patches based on spatial aggregation analysis provided in the embodiments of this specification may include at least:

[0039] (1) By rasterizing the vector data of the soil and water loss spots in the target area, the data volume can be reduced to a certain extent, the processing efficiency of the soil and water loss spot data can be effectively improved, and the shortcomings of the traditional raster data in terms of spatial relationships can be compensated. In addition, in the embodiment of the present application, the soil and water loss spot data after rasterizing the vector data of the soil and water loss spot data is conducive to spatial analysis and calculation in the subsequent process, thereby providing basic data support for the subsequent spatial aggregation analysis, and facilitating the comprehensive mapping and scientific management of the soil and water loss spots;

[0040] (2) By calculating the similarity between each grid cell and its adjacent grid cells based on the soil erosion information, land use type information, and DEM information corresponding to each grid cell, the aggregation characterization parameters of the soil and water loss patch corresponding to each grid cell are obtained. This can improve the accuracy of soil and water loss patch boundary determination in the subsequent process, thereby improving the precision and accuracy of soil and water loss patch boundaries;

[0041] (3) By analyzing the spatial aggregation of soil and water loss patches, we can obtain the spatial aggregation characteristics of the target area. Based on the spatial aggregation characteristics, we can draw the aggregation analysis image of the soil and water loss patches, which can more accurately identify the key areas of soil and water loss problems and provide strong support for subsequent governance and planning.

[0042] (4) By using straight lines to connect a plurality of continuous boundary points that are in a linear position relationship and the distance between two adjacent boundary points is less than a third preset threshold, and using smooth curves to connect the boundary points in other cases, the jagged edges at the boundaries can be avoided, thereby more effectively reflecting the actual boundary conditions of the soil and water loss map, and to a certain extent improving the mapping accuracy and display effect of the soil and water loss map;

[0043] (5) By adaptively adjusting the curvature of the boundary line and optimizing the boundary line according to the size of the cluster and the distribution density of the soil erosion patches corresponding to the cluster, the final generated boundary line can be closer to the actual soil erosion situation and mapping requirements.

[0044] Additional features are described in part in the following description. They will become apparent to those skilled in the art by reviewing the following and accompanying drawings, or by following the production or operation of the examples. The features of this specification may be realized and obtained by practicing or using the various aspects of the methods, tools, and combinations described in the following detailed examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same numbers represent the same structures, wherein:

[0046] Figure 1 is an exemplary flow chart of a method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis according to some embodiments of this specification;

[0047] Figure 2 is a schematic diagram of the relationship between a target grid cell and adjacent grid cells according to some embodiments of this specification;

[0048] Figure 3 This is a flowchart of exemplary sub-steps of a method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis according to some embodiments of this specification;

[0049] Figure 4 This is a schematic diagram of the boundary point connection effect shown in some embodiments of this specification. DETAILED DESCRIPTION

[0050] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0051] It should be understood that the terms "system," "device," "unit," and / or "module" used in this specification are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0052] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0053] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0054] The comprehensive mapping method for soil and water loss patches based on spatial aggregation analysis provided in the embodiments of this specification is described in detail below with reference to the accompanying drawings.

[0055] Figure 1 This is an exemplary flow chart of a method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis according to some embodiments of this specification. In some embodiments, the method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis can be executed by processing logic, which can include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to perform hardware simulation), etc., or any combination thereof. In some embodiments, Figure 1 One or more operations in the flowchart of the method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis can be implemented by a processing device and / or a terminal device. For example, the method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis can be stored in a storage device in the form of a computer program and / or instructions, and can be called and / or executed by the processing device and / or terminal device.

[0056] Reference Figure 1 The method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis provided in the embodiments of the present application may include:

[0057] Step S110, obtaining soil and water loss spot vector data of the target area, and rasterizing the soil and water loss spot vector data to obtain raster data corresponding to each raster unit, wherein the raster data corresponding to each raster unit at least includes soil erosion information, land use type information and DEM information.

[0058] In this specification, a target area can refer to any geographical area where soil erosion needs to be monitored, such as one or more counties, cities, provinces, or countries. A soil erosion patch refers to a spatially continuous geographical unit with the same land use type, basically consistent soil erosion type and intensity.

[0059] The soil and water loss patch vector data of the target area can be understood as geographic information data represented in vector format based on the spatial distribution of soil and water loss phenomena. These geographic information data can be obtained through remote sensing technology, geographic information systems, or other geographic data collection methods. Specifically, in some embodiments, these vector data can be formed by performing spatial analysis on the soil and water loss area to form one or more regular or irregular polygons, i.e., patches, each of which represents an area with specific soil and water loss characteristics. Specifically, the soil and water loss patch vector data may include the following elements:

[0060] (1) Geometric information: Each patch is represented by a geometric figure connected by multiple vertices, usually in the form of a polygon. Each polygon represents a specific area where soil erosion and loss are caused by natural factors (such as rainfall, wind, topography, etc.) or human activities (such as overcultivation and deforestation). It can reflect the characteristics of soil and water loss within a certain range (such as erosion, siltation, loss, etc.).

[0061] (2) Attribute information: In addition to geometric information, each map block also has some attribute data, such as the type of soil erosion (such as wind erosion, water erosion, etc.), severity (such as mild, moderate, severe), erosion amount, land use type, DEM (Digital Elevation Model) information, etc.

[0062] (3) Spatial analysis: These vector data can be used for further spatial analysis, such as assessing potential risk areas for soil erosion, monitoring dynamic changes, and formulating prevention and control measures by overlaying geographic data at different levels.

[0063] (4) Coordinate reference system: These vector data are usually calibrated according to a specific coordinate reference system (such as WGS84, UTM, etc.) to ensure the spatial accuracy and consistency of the data.

[0064] In the embodiment of the present application, the soil and water loss map vector data can be rasterized to obtain raster data corresponding to each grid cell. It is understood that in the embodiment of the present application, the rasterization process refers to dividing the soil and water loss map vector data into a number of regular grid cells according to a certain spatial resolution, each grid cell representing a grid. By performing statistics and analysis on the soil and water loss map vector data within each grid, the soil and water loss characteristic information corresponding to each grid can be obtained, thereby providing basic data support for subsequent spatial aggregation analysis and comprehensive mapping.

[0065] Specifically, in some embodiments of the present application, the grid data corresponding to each grid cell includes at least soil erosion information, land use type information and DEM information. Among them, the soil erosion information can be used to reflect the degree of erosion (such as mild, moderate, severe) and type (such as wind erosion, water erosion, etc.) of the soil of the corresponding grid cell. The land use type information can indicate the use mode of the land corresponding to each grid cell, for example, woodland, grassland, cultivated land, etc. DEM (Digital Elevation Model) information can describe the topographic features of the land corresponding to each grid cell, including terrain height, slope, slope direction, etc.

[0066] It should be noted that, in the embodiment of the present application, the grid data corresponding to each grid cell can be understood as the attribute information of the soil and water loss patch corresponding to the grid cell (each grid cell is a point in a soil and water loss patch, and each grid cell can be regarded as a very small patch in this specification). The following is a brief description of the rasterization process involved in the embodiment of the present application:

[0067] In some embodiments of the present application, first, the boundary coordinates of the soil and water loss map vector data can be converted into grid coordinates; then, the soil and water loss map is divided into corresponding grid units according to the grid coordinates, and each grid unit is assigned attribute information of the soil and water loss map to form rasterized soil and water loss map data, wherein the attribute information of the soil and water loss map includes at least soil erosion information, land use type information and DEM information.

[0068] Among them, the boundary coordinates of the soil and water erosion map vector data are usually longitude and latitude coordinates or plane rectangular coordinates. These coordinate information can accurately describe the spatial position and shape of the soil and water erosion map. During the rasterization process, these boundary coordinates need to be converted into coordinates under the grid coordinate system so as to divide the soil and water erosion map into corresponding grid cells. During the conversion process, the coordinate system needs to be transformed and the grid cells need to be divided to ensure that each grid cell can accurately reflect the spatial characteristics of the soil and water erosion map. More details about the rasterization process can be regarded as prior art and will not be discussed in detail in this specification.

[0069] It should be noted that in the embodiments of the present application, by rasterizing the target area's soil and water loss patch vector data, the data volume can be reduced to a certain extent, effectively improving the processing efficiency of the soil and water loss patch data and compensating for the shortcomings of traditional raster data in terms of spatial relationships. In addition, the rasterized soil and water loss patch data facilitates spatial analysis and calculations in subsequent processes, thereby providing basic data support for subsequent spatial aggregation analysis and facilitating the comprehensive mapping and scientific management of soil and water loss patches.

[0070] Step S120 : calculating, based on the grid data, a concentration characteristic parameter of the soil and water loss patch corresponding to each grid unit.

[0071] In some embodiments of the present application, the similarity of the attribute information of the soil and water erosion patches corresponding to each grid cell and the adjacent grid cells can be calculated based on the grid data; then, the aggregation characterization parameters of the soil and water erosion patches corresponding to each grid cell are determined according to the similarity.

[0072] Specifically, in an embodiment of the present application, the adjacent grid cells may refer to grid cells within a 3*3 neighborhood (i.e., an 8-neighborhood), a 5*5 neighborhood (i.e., a 24-neighborhood), or other neighborhood ranges. Among them, an 8-neighborhood represents 8 grid cells around a grid cell, and a 24-neighborhood represents 24 grid cells around a grid cell. By calculating the similarity between each target grid cell and the adjacent grid cells, a similarity sequence can be obtained. In some embodiments of the present application, the similarity sequence can be used as a parameter representing the aggregation of the soil and water loss pattern corresponding to the target grid cell.

[0073] In the embodiments of the present application, the similarity is calculated by any one of cosine similarity, Euclidean distance, or Manhattan distance. Taking Euclidean distance as an example, in some embodiments of the present application, the similarity calculation process can be expressed as follows:

[0074]

[0075] S = 1-norm(d(P1, P2))

[0076] Among them, d(P1, P2) represents the distance between the attribute information of the soil erosion map corresponding to the grid cell P1 and the adjacent grid cell P2; x2 represents the soil erosion information corresponding to the adjacent grid cell P2, and x1 represents the soil erosion information corresponding to the grid cell P1; y2 represents the land use type information corresponding to the adjacent grid cell P2, and y1 represents the land use type information corresponding to the grid cell P1; z2 represents the DEM information corresponding to the adjacent grid cell P2, and z1 represents the DEM information corresponding to the grid cell P1; S represents the similarity of the attribute information of the soil erosion map corresponding to the grid cell P1 and the adjacent grid cell P2; norm represents the normalization operation. It should be noted that in the embodiment of the present application, the aforementioned soil erosion information and land use type information can be mapped to specific numerical values through preset mapping rules to facilitate the calculation of similarity. For example, soil erosion information can be divided into different levels according to erosion intensity, and each level corresponds to a specific numerical value; similarly, land use type information can also be divided into different levels according to different types and assigned corresponding numerical values. In the embodiment of the present application, the DEM information can be directly calculated using its elevation value.

[0077] In some embodiments, for each target grid cell, the similarities between each adjacent grid cell and the target grid cell can be arranged in a specified order to obtain a similarity sequence, and the similarity sequence is used as a clustering characterization parameter of the soil and water loss map corresponding to the target grid cell.

[0078] For example, referring to Figure 2 Taking the target grid cell A as an example, if the adjacent grid cells are within the 3*3 neighborhood (i.e., 8 neighborhoods), we can start from grid cell B and sort in a clockwise direction to arrange the similarity of the attribute information of the soil and water loss patches corresponding to each adjacent grid cell and the target grid cell A to obtain the similarity sequence X A =(S1, S2, S3, S4, S5, S6, S7, S8), where S1 is the similarity of the attribute information of the soil and water loss patches corresponding to the target grid cell A and grid cell B.

[0079] Similarly, in some embodiments, if the adjacent grid cells are grid cells within a 5*5 neighborhood (ie, a 24-neighborhood), the similarity sequence can be expressed as X A =(S1, S2, S3, S4, S5, S6, S7, S8, S9,…,S 24), where S1 is the similarity of the attribute information of the soil and water loss patches corresponding to the target grid cell A and grid cell B, and S9 is the similarity of the attribute information of the soil and water loss patches corresponding to the target grid cell A and grid cell C.

[0080] In some embodiments, the possibility that each target grid cell is a patch boundary can be further determined based on the similarity sequence. For example, the similarities in the similarity sequence can be summed to obtain a sum of similarities. If the sum of similarities corresponding to the target grid cell is smaller, it means that the difference in attribute information of the soil and water loss patch corresponding to the target grid cell and at least part of the adjacent grid cells is greater, and the possibility that the target grid cell is a patch boundary is higher; conversely, if the sum of similarities of the target grid cell is larger, it means that the attribute information of the soil and water loss patch corresponding to the target grid cell and the adjacent grid cells is more similar, and the possibility that the target grid cell is a data point inside the patch is higher.

[0081] It should be pointed out that in the embodiment of the present application, by calculating the similarity between each grid cell and its adjacent grid cells based on the soil erosion information, land use type information and DEM information corresponding to each grid cell, the aggregation characterization parameters of the soil and water loss map corresponding to each grid cell are obtained, which can improve the accuracy of the soil and water loss map boundary judgment in the subsequent process, thereby improving the precision and accuracy of the soil and water loss map boundary.

[0082] Step S130: determine the spatial aggregation characteristics of the soil and water erosion patches in the target area according to the aggregation characterization parameters, and draw an aggregation analysis image of the soil and water erosion patches according to the spatial aggregation characteristics, wherein the spatial aggregation characteristics are used to reflect the spatial distribution characteristics and boundary position characteristics of the soil and water erosion patches in one or more sub-areas contained in the target area.

[0083] In some embodiments of the present application, the adjacent grid cells and the target grid cell with a similarity greater than a first preset threshold can be divided into a cluster based on the aggregation characterization parameter, and the clusters with soil and water loss patches intersection within a preset range can be merged into a larger cluster until there are no clusters that can be merged, thereby obtaining one or more soil and water loss patch clusters. Among them, the first preset threshold can be set according to actual needs (for example, it can be set to), and it is not specifically limited in this specification. In some embodiments, the preset range can be set to 80 grid cells. It should be noted that the preset range is only for exemplary description. In some other embodiments, the preset range can be other values, such as 50 grid cells, 100 grid cells, etc.

[0084] Furthermore, in an embodiment of the present application, each soil and water loss patch cluster can be regarded as a sub-region of the target area (i.e., a patch area in the target area), and the spatial clustering characteristics of the soil and water loss patches of each sub-region contained in the target area are determined based on the soil and water loss patch cluster. In an embodiment of the present application, the spatial clustering characteristics may include spatial distribution characteristics and boundary position characteristics, wherein the spatial distribution characteristics include the size of each cluster and the distribution density of the soil and water loss patches within each cluster, and the boundary position characteristics include the boundary position coordinates corresponding to each cluster.

[0085] Specifically, in some embodiments of the present application, ArcGIS (a comprehensive geographic information system (GIS) software platform that includes multiple components and can be applied to map production, spatial data analysis, geographic data visualization, and geographic information management) can be used to extract key grid cells located at the boundary of each soil and water loss patch cluster as boundary points, and the boundary position coordinates corresponding to each soil and water loss patch cluster can be obtained based on the position coordinates of the boundary points. Among them, the process of using ArcGIS to extract boundary points can be regarded as prior art, and its specific implementation process will not be described in detail in this specification.

[0086] Furthermore, for each soil and water erosion patch cluster, the size of the corresponding cluster can be obtained by counting the number of all grid cells (some of which may not belong to the soil and water erosion patch, for example, may correspond to areas where there is no soil and water erosion problem) contained in the corresponding boundary position coordinate range (the boundary position coordinate range can be determined by linearly connecting each boundary point in sequence), and by calculating the ratio of the number of grid cells corresponding to the soil and water erosion patch within the boundary position coordinate range to the size of the cluster to which it belongs, the distribution density of the soil and water erosion patch within each cluster can be obtained.

[0087] It can be understood that the larger the cluster size, the wider the area covered by the soil erosion patch cluster; and the higher the distribution density of soil erosion patches within a cluster, the more serious the soil erosion problem within the cluster, requiring greater attention and attention. In the embodiments of the present application, by performing spatial clustering analysis on soil erosion patches, key areas of soil erosion problems can be more accurately identified, providing strong support for subsequent governance and planning.

[0088] Furthermore, in embodiments of the present application, an aggregation analysis image of the soil and water loss patches can be drawn based on the spatial aggregation features. For example, in some embodiments of the present application, for each of the soil and water loss patch clusters, a corresponding boundary line can be determined based on the size of the corresponding cluster, the boundary position coordinates, and the distribution density of the corresponding soil and water loss patches, thereby obtaining an aggregation analysis image of the corresponding soil and water loss patches.

[0089] It should be noted that in the embodiment of the present application, the aggregation analysis image can be understood as a visualization image that divides the specific areas and ranges corresponding to each soil and water loss patch cluster by boundary lines to reflect the spatial aggregation characteristics of the soil and water loss patches.

[0090] Specifically, refer to Figure 3 In some embodiments of the present application, the process of determining the corresponding boundary line based on the size of the cluster, the boundary position coordinates, and the distribution density of the corresponding soil erosion patches may include the following steps:

[0091] Step S131 : obtaining the position coordinates of all boundary points in the target cluster, and connecting all boundary points in the target cluster by smooth curves and / or straight lines to obtain initial boundary lines.

[0092] Step S132: searching for target adjacent boundary points whose distance is greater than a second preset threshold, and optimizing the initial boundary line at the target adjacent boundary points based on the size of the cluster and the distribution density of the soil and water loss patches to obtain a target boundary line.

[0093] Specifically, in step S131, for a plurality of consecutive boundary points that are in a linear position relationship and whose distance between two adjacent boundary points is less than a third preset threshold, they may be connected by a straight line, and vice versa, they may be connected by a smooth curve. The third preset threshold may be set to 4 grid units.

[0094] It should be understood that the third preset threshold is merely an example, and in other embodiments of the present application, the third preset threshold may also be set to other values, such as 5 grid units, 8 grid units, etc.

[0095] In the embodiment of the present application, the smooth curve connection process can be implemented using Bezier curves, spline interpolation, B-spline curves, etc. The specific implementation methods of using the above-mentioned various methods to smoothly connect each boundary point can be regarded as existing technologies and will not be described in detail in this specification.

[0096] It should be pointed out that in the embodiment of the present application, by using straight lines to connect multiple continuous boundary points that are in a linear position relationship and the distance between two adjacent boundary points is less than a third preset threshold, and by using smooth curves to connect the boundary points in other situations, the jagged appearance of the boundary can be avoided, thereby more effectively reflecting the actual boundary conditions of the soil and water erosion map, and to a certain extent improving the mapping accuracy and display effect of the soil and water erosion map.

[0097] Furthermore, considering that in the process of connecting boundary points using a smooth curve, the curve may be too curved or too smooth, resulting in an inaccurate boundary line, in step S132, it is necessary to find target adjacent boundary points whose distance is greater than a second preset threshold, and optimize the boundary lines at these target adjacent boundary points based on the size of the cluster and the distribution density of the soil erosion patches. The second preset threshold can be set according to the actual accuracy requirements. As an example only, in some embodiments of the present application, the second preset threshold can be set to 20 grid units.

[0098] Specifically, in an embodiment of the present application, the target adjacent boundary points include a first boundary point and a second boundary point belonging to a first type. The process of searching for target adjacent boundary points whose distance is greater than a second preset threshold and optimizing the initial boundary line at the target adjacent boundary points based on the size of the cluster and the distribution density of the soil and water loss patches may include the following steps:

[0099] First, obtain a third boundary point located between the first boundary point and the second boundary point, and whose first distance relative to the first boundary point and second distance relative to the second boundary point meet the preset conditions, wherein the third boundary point belongs to the second type. In an embodiment of the present application, the aforementioned first type and second type may refer to different types of boundary points, for example, the first type may refer to a concave boundary point, and the second type may refer to a convex boundary point, wherein a concave boundary point refers to a point on the boundary line that is concave inward, and a convex boundary point refers to a point on the boundary line that is convex outward. In some embodiments of the present application, the preset condition may refer to the difference between the first distance and the second distance being less than 50% of the smaller of the first distance and the second distance.

[0100] Furthermore, in some embodiments of the present application, the first boundary point, the third boundary point, and the second boundary point can be sequentially connected based on a Bezier curve, and the initial boundary line at the target adjacent boundary point can be replaced based on the connection results to obtain a target boundary line at the target adjacent boundary point. The curvature of the Bezier curve is negatively correlated with the size of the cluster and positively correlated with the distribution density of the soil and water loss patches.

[0101] like Figure 4 As shown, taking M1 as the first boundary point and M2 as the second boundary point as an example, a third boundary point M3 located between the first boundary point M1 and the second boundary point M2, and having a first distance relative to the first boundary point and a second distance relative to the second boundary point satisfying the preset conditions (M1 and M2 are convex boundary points, and M3 is a concave boundary point) can be obtained. Then, based on the Bezier curve, the first boundary point M1, the third boundary point M3 and the second boundary point M2 are connected in sequence, and the initial boundary line at the target adjacent boundary point is replaced according to the connection result, thereby obtaining the target boundary line at the target adjacent boundary point.

[0102] It should be noted that in an embodiment of the present application, in the process of sequentially connecting the first boundary point M1, the third boundary point M3 and the second boundary point M2 based on the Bezier curve, the curvature of the line connecting the first boundary point M1, the third boundary point M3 and the second boundary point M2 can be adaptively adjusted according to the size of the cluster to which they belong and the distribution density of the soil erosion patches corresponding to the cluster.

[0103] Specifically, in some embodiments, the relationship between the curvature, the size of the cluster, and the distribution density of the soil and water loss patches corresponding to the cluster can be expressed by the following formula:

[0104]

[0105] Wherein, k represents the curvature calculated based on the size of the cluster and the distribution density of the soil erosion patches corresponding to the cluster; Q represents the size of the cluster, and U represents the distribution density of the soil erosion patches corresponding to the cluster; a and b are both preset constants. As an example only, in some embodiments, a and b can both be set to 2.

[0106] This formula enables adaptive adjustment of curvature, so that when the clusters are small and the density of soil and water loss patches is high, the curvature of the boundary line is large, while when the clusters are large and the density of soil and water loss patches is low, the curvature of the boundary line is small. It can be understood that clusters with large areas and high density of soil and water loss patches are areas of focus. By using a larger curvature to generate the boundary line, the boundary of the area can be more finely delineated, improving mapping accuracy. Conversely, when the clusters are small and the density of soil and water loss patches is low, by using a smaller curvature to generate the boundary line, it is possible to avoid excessive curvature and resulting in overly complex boundary lines, thereby reducing mapping difficulty and computational complexity while ensuring mapping quality.

[0107] In the embodiment of the present application, after the curvature is calculated in the above manner, the corresponding radius of curvature can be calculated (the smaller the curvature, the larger the radius of curvature), and then a Bezier curve is drawn based on the curvature radius to obtain a smooth and continuous boundary line that meets the requirements. It should be pointed out that by optimizing the initial boundary line at the target adjacent boundary points in this way, not only the overall size of the soil erosion patch is taken into account, but also the distribution characteristics of the soil erosion patch are fully combined, so that the final generated boundary line is closer to the actual soil erosion situation and mapping requirements.

[0108] After determining the boundary line of each soil and water loss patch cluster through the above steps, an aggregation analysis image of the soil and water loss patches can be obtained.

[0109] Further, continue to refer to Figure 1 After step S130, the method may further include:

[0110] Step S140 : using the aggregation analysis image of the soil and water loss patches as a reference, hierarchically expressing the soil and water loss patches and creating layers, and synthesizing a final soil and water loss patch comprehensive map.

[0111] Specifically, in the embodiments of the present application, a corresponding comprehensive index can be calculated based on the size of each soil and water loss patch cluster and the distribution density of its corresponding soil and water loss patch. For example, in some embodiments, the calculation process of the comprehensive index can be expressed by the following formula:

[0112] Z=α*Q+β*U

[0113] Among them, Z represents the comprehensive index corresponding to the cluster of soil and water loss patches (the comprehensive index can be used to measure the severity of the cluster of soil and water loss patches and its potential impact on the ecological environment); Q represents the size of the cluster, and U represents the distribution density of the soil and water loss patches corresponding to the cluster; α and β are preset weight coefficients. In the embodiment of the present application, they can be adjusted according to actual needs to balance the contribution of the size Q of the cluster and the distribution density U of the soil and water loss patches corresponding to the cluster in the calculation of the comprehensive index.

[0114] Furthermore, in an embodiment of the present application, each soil erosion spot cluster can be divided into different levels according to the comprehensive index, and a corresponding layer can be created for each level of soil erosion spots. For example, a soil erosion spot cluster with a higher comprehensive index can be divided into a high level, indicating that its potential impact on the ecological environment is greater and needs to be given priority for governance; while a soil erosion spot cluster with a lower comprehensive index can be divided into a low level, indicating that its potential impact on the ecological environment is relatively small and can be deferred for governance or simpler governance measures can be taken. In an embodiment of the present application, different levels of soil erosion spot clusters can be given different colors or marks to facilitate intuitive distinction and display. For example, a high-level soil erosion spot cluster can be marked with red or other dark colors, while a low-level soil erosion spot cluster can be marked with green, yellow or other light colors, so that relevant personnel can quickly identify which areas have more serious soil erosion and need to be given priority for governance, and which areas have relatively light soil erosion and can be deferred for governance or simpler governance measures can be taken. In addition, in the embodiment of the present application, the soil and water loss patch clusters of different levels can be further subdivided according to actual needs, so as to more accurately evaluate their potential impact on the ecological environment and formulate corresponding treatment plans.

[0115] It can be understood that through hierarchical expression and layer creation, the severity and distribution of soil erosion spots can be displayed more intuitively, thus providing strong support for subsequent governance work.

[0116] Furthermore, in embodiments of the present application, the soil erosion patch layers of different levels can be superimposed to obtain a comprehensive soil erosion patch map. It should be noted that in some embodiments of the present application, the comprehensive soil erosion patch map can include multiple layers, each layer corresponding to a different soil erosion patch level, so as to facilitate a comprehensive analysis and assessment of the soil erosion situation as needed.

[0117] In some embodiments of the present application, the comprehensive map of soil erosion patches can also be associated with other relevant data, such as rainfall, soil type, vegetation coverage, etc., so as to further enrich the content of soil erosion assessment and improve the accuracy and reliability of the assessment.

[0118] In addition, in some embodiments of the application, the soil erosion patch comprehensive map can also be integrated with the spatial coordinate system to make the geographical location information of the soil erosion patch clearer and the spatial relationship clearer, so as to accurately locate and display the soil erosion situation on the map. It can be understood that this integration not only helps relevant personnel to more intuitively understand the spatial distribution characteristics of soil erosion, but also provides a more accurate spatial reference for the formulation of targeted control measures. In this way, a comprehensive, accurate and efficient assessment of the soil erosion situation can be achieved, providing more powerful decision-making support for soil erosion prevention and control work.

[0119] In summary, the beneficial effects that may be brought about by the embodiments of this specification include but are not limited to: (1) In the soil and water loss patch comprehensive mapping method based on spatial aggregation analysis provided in some embodiments of this specification, by rasterizing the soil and water loss patch vector data of the target area, the data volume can be reduced to a certain extent, the processing efficiency of the soil and water loss patch data can be effectively improved, and the shortcomings of traditional raster data in terms of spatial relationships can be compensated. In addition, in the embodiments of this application, the soil and water loss patch data after rasterizing the soil and water loss patch vector data is conducive to spatial analysis and calculation in the subsequent process, thereby providing a basis for subsequent The spatial aggregation analysis provides basic data support, which facilitates the comprehensive mapping and scientific management of soil and water loss patches; (2) In the comprehensive mapping method of soil and water loss patches based on spatial aggregation analysis provided in some embodiments of this specification, by calculating the similarity between each grid cell and its adjacent grid cells based on the soil erosion information, land use type information and DEM information corresponding to each grid cell, the aggregation characterization parameters of the soil and water loss patches corresponding to each grid cell are obtained, which can improve the accuracy of the soil and water loss patch boundary determination in the subsequent process, thereby improving the precision and accuracy of the soil and water loss patch boundary; (3) In this specification In the method for comprehensively mapping water and soil erosion patches based on spatial aggregation analysis provided in some embodiments of this specification, by performing spatial aggregation analysis on water and soil erosion patches, the spatial aggregation characteristics of the target area are obtained, and the aggregation analysis image of the water and soil erosion patches is drawn according to the spatial aggregation characteristics, which can more accurately identify the key areas of water and soil erosion problems and provide strong support for subsequent governance and planning; (4) In the method for comprehensively mapping water and soil erosion patches based on spatial aggregation analysis provided in some embodiments of this specification, by using a plurality of straight lines that are in a linear position relationship and the distance between two adjacent boundary points is less than a third preset threshold, The continuous boundary points are connected by smooth curves, and the boundary points of other situations are connected by smooth curves, which can avoid the jagged appearance of the boundary, thereby more effectively reflecting the actual boundary conditions of the soil and water loss patches, and to a certain extent improving the mapping accuracy and display effect of the soil and water loss patches; (5) In the soil and water loss patch comprehensive mapping method based on spatial aggregation analysis provided in some embodiments of this specification, by adaptively adjusting the curvature of the boundary line and optimizing the boundary line according to the size of the cluster and the distribution density of the soil and water loss patches corresponding to the cluster, the boundary line finally generated can be closer to the actual soil and water loss situation and mapping requirements.

[0120] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other possible beneficial effects.

[0121] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0122] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0123] In addition, it will be understood by those skilled in the art that various aspects of this specification may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of this specification may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of this specification may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0124] A computer storage medium may include a propagated data signal embodying the computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, or any suitable combination thereof. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium that can be connected to an instruction execution system, apparatus, or device to communicate, propagate, or transfer the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of these.

[0125] The computer program codes required for the operation of the various parts of this specification can be written in any one or more programming languages, including object-oriented programming languages such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages such as C, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages. The program code can be run entirely on the user's computer, or as a separate software package on the user's computer, or partly on the user's computer and partly on a remote computer, or entirely on a remote computer or processing device. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0126] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some embodiments of the invention that are currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing processing device or mobile device.

[0127] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0128] In some embodiments, numbers are used to describe the quantity of components and attributes. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may change according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the settings of such numerical values are as accurate as possible within the feasible range.

[0129] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, and documents, cited in this specification is hereby incorporated by reference in its entirety. This includes application history documents that are inconsistent with or conflict with the content of this specification, as well as documents (currently or subsequently attached to this specification) that limit the broadest scope of the claims of this specification. It should be noted that if the descriptions, definitions, and / or terminology used in the accompanying materials are inconsistent or conflicting with the content of this specification, the descriptions, definitions, and / or terminology used in this specification will control.

[0130] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A comprehensive mapping method for soil and water loss patches based on spatial aggregation analysis, characterized in that: include: Obtaining soil and water loss spot vector data of the target area, and performing rasterization processing on the soil and water loss spot vector data to obtain raster data corresponding to each raster unit, wherein the raster data corresponding to each raster unit includes at least soil erosion information, land use type information, and DEM information; Calculating the aggregation characterization parameter of the soil and water loss patch corresponding to each grid unit based on the grid data; Determining spatial aggregation characteristics of the soil and water erosion patches in the target area according to the aggregation characterization parameters, and drawing an aggregation analysis image of the soil and water erosion patches according to the spatial aggregation characteristics, wherein the spatial aggregation characteristics are used to reflect the spatial distribution characteristics and boundary position characteristics of the soil and water erosion patches in one or more sub-areas contained in the target area; Using the aggregation analysis image of the soil and water loss patches as a reference, the soil and water loss patches are hierarchically expressed and layers are created, and a final soil and water loss patch comprehensive map is synthesized.

2. The method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis according to claim 1, characterized in that: The rasterizing of the soil and water loss map vector data includes: Converting the boundary coordinates of the soil and water loss map vector data into grid coordinates; The soil and water loss map is divided into corresponding grid units according to the grid coordinates, and attribute information of the soil and water loss map is assigned to each grid unit to form rasterized soil and water loss map data, wherein the attribute information of the soil and water loss map includes at least soil erosion information, land use type information and DEM information.

3. The method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis according to claim 2, characterized in that: The calculating, based on the grid data, the aggregation characterization parameter of the soil and water loss patch corresponding to each grid unit includes: Calculating the similarity of attribute information of each grid cell and the soil erosion patches corresponding to adjacent grid cells based on the grid data, wherein the similarity is calculated using any one of cosine similarity, Euclidean distance, and Manhattan distance; The aggregation characterization parameter of the soil and water loss patch corresponding to each grid unit is determined according to the similarity.

4. The method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis according to claim 3, characterized in that: The step of determining the aggregation characterization parameter of the soil and water loss patch corresponding to each grid unit according to the similarity includes: For each target grid cell, the similarities between each adjacent grid cell and the target grid cell are arranged in a specified order to obtain a similarity sequence, and the similarity sequence is used as the aggregation characterization parameter of the soil and water loss map corresponding to the target grid cell.

5. The method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis according to claim 4, characterized in that: The determining of the spatial aggregation characteristics of the soil and water loss patches in the target area according to the aggregation characterization parameter includes: Based on the aggregation characterization parameter, the adjacent grid cells and the target grid cell whose similarity is greater than a first preset threshold are divided into one cluster, and the clusters having the soil and water loss patches intersecting within a preset range are merged into a larger cluster until there are no clusters that can be merged, thereby obtaining one or more soil and water loss patch clusters; Each soil and water loss patch cluster is regarded as a sub-area of the target area, and the spatial clustering characteristics of the soil and water loss patches of each sub-area contained in the target area are determined based on the soil and water loss patch clusters, wherein the spatial clustering characteristics include spatial distribution characteristics and boundary position characteristics, the spatial distribution characteristics include the size of each cluster and the distribution density of the soil and water loss patches within each cluster, and the boundary position characteristics include the boundary position coordinates corresponding to each cluster.

6. The method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis according to claim 5, characterized in that: The determining of the spatial aggregation characteristics of the soil and water loss patches of each sub-region included in the target region based on the soil and water loss patch clusters includes: Using ArcGIS, key grid cells at the boundary of each soil and water loss patch cluster are extracted as boundary points, and the boundary position coordinates of each soil and water loss patch cluster are obtained based on the position coordinates of the boundary points. For each soil and water loss patch cluster, the size of the corresponding cluster is obtained by counting the number of all grid cells contained in the corresponding boundary position coordinate range, and the distribution density of the soil and water loss patches in each cluster is obtained by calculating the ratio of the number of grid cells corresponding to the soil and water loss patches within the boundary position coordinate range to the size of the cluster to which they belong.

7. The method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis according to claim 5, characterized in that: Drawing an aggregation analysis image of soil and water loss patches according to the spatial aggregation characteristics includes: For each of the soil and water loss patch clusters, a corresponding boundary line is determined based on the size of the corresponding cluster, the boundary position coordinates, and the distribution density of the corresponding soil and water loss patch, to obtain an aggregation analysis image of the soil and water loss patch.

8. The method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis according to claim 7, characterized in that: The step of determining a corresponding boundary line for each of the soil and water loss patch clusters based on the size of the corresponding cluster, the boundary position coordinates, and the distribution density of the corresponding soil and water loss patch includes: Obtaining the position coordinates of all boundary points in the target cluster, and connecting all boundary points in the target cluster by smooth curves and / or straight lines to obtain an initial boundary line; Searching for target adjacent boundary points whose distance is greater than a second preset threshold, and optimizing the initial boundary line at the target adjacent boundary point based on the size of the cluster and the distribution density of the soil and water loss patches to obtain a target boundary line.

9. The method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis according to claim 8, characterized in that: The connecting all boundary points of the target cluster by smooth curves and / or straight lines includes: For a plurality of continuous boundary points that are in a linear position relationship and the distance between two adjacent boundary points is less than a third preset threshold, they are connected by a straight line; otherwise, they are connected by a smooth curve; The target adjacent boundary points include a first boundary point and a second boundary point of a first type, searching for target adjacent boundary points whose distance is greater than a second preset threshold, and optimizing the initial boundary line at the target adjacent boundary points based on the size of the cluster and the distribution density of the soil and water loss patches, including: Acquire a third boundary point located between the first boundary point and the second boundary point, wherein a first distance relative to the first boundary point and a second distance relative to the second boundary point satisfy a preset condition, wherein the third boundary point belongs to the second type; The first boundary point, the third boundary point, and the second boundary point are sequentially connected based on a Bezier curve, and the initial boundary line at the target adjacent boundary point is replaced according to the connection result; wherein the curvature of the Bezier curve is negatively correlated with the size of the cluster and positively correlated with the distribution density of the soil and water loss patch.

10. The method for comprehensive mapping of soil and water loss patches based on spatial aggregation analysis according to claim 7, characterized in that: The method uses the aggregation analysis image of the soil and water loss patches as a reference, hierarchically expresses the soil and water loss patches and creates layers, and synthesizes the final soil and water loss patch comprehensive map, including: The corresponding comprehensive index is calculated based on the size of each soil and water loss patch cluster and the distribution density of its corresponding soil and water loss patch; Classifying the soil and water loss patch clusters into different levels according to the comprehensive index, and creating a corresponding layer for each level of soil and water loss patch; The soil and water loss map layers of different levels are superimposed to obtain the soil and water loss map comprehensive map.

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