Method and device for constructing mangrove ecological protection zone distribution map and computer equipment

Abstract

This invention relates to the field of mangrove ecological analysis technology, and in particular to a method, apparatus, and computer equipment for constructing a distribution map of mangrove ecological reserves. It utilizes multidimensional feature data constructed from UAV multispectral imagery and elevation model data to build a geospatial structure map and edge weight matrix for patch aggregation. Combining the edge weight matrix, a normalized cut model is introduced to find the globally optimal segmentation scheme. While considering multidimensional feature data, physical geographical resistance constraints composed of real topography are forcibly applied, dividing the geospatial structure map into multiple geographical unit sub-maps. This generates a spatially continuous, internally homogeneous, and strictly controlled spatial analysis base map, used for identifying mangrove ecological reserves, thereby improving the accuracy and efficiency of mangrove ecological reserve identification.

Description

Technical Field

[0001] This invention relates to the field of mangrove ecological analysis technology, and in particular to a method, apparatus, computer equipment, and storage medium for constructing a distribution map of a mangrove ecological reserve. Background Technology

[0002] The refined division of geographical units is a prerequisite for the ecological protection, restoration, and measurement of national land space. In the practical physical application of mangrove ecological reserve identification, the basic division of geographical units is crucial.

[0003] However, when identifying complex coastal habitats (such as intertidal areas like Zhenhai Bay), existing technologies, lacking global graph theory topological constraints, easily generate numerous fragmented and scattered patches. These patches cannot spontaneously aggregate to form contiguous geographical units with practical management significance. Subsequent manual patch fusion and topological revision require substantial human resources. Furthermore, due to the lack of topological constraints on actual geographical barriers such as tidal channels and steep slopes, the delineated geographical units easily cross physical barriers, resulting in severe breaks in the geographical connectivity and ecological logic of habitat patches. This deficiency negatively impacts subsequent ecological reserve identification, leading to the missegmentation of contiguous communities and boundary delineation that deviates significantly from actual physical topology, resulting in highly inaccurate ecological reserve identification. Summary of the Invention

[0004] Based on this, the purpose of this invention is to provide a method, apparatus, computer equipment, and storage medium for constructing a distribution map of mangrove ecological reserves. This method utilizes multidimensional feature data constructed from UAV multispectral imagery and elevation model data to construct a geospatial structure map and edge weight matrix for patch aggregation. Combining the edge weight matrix, a normalized cut model is introduced to find the globally optimal segmentation scheme. While considering multidimensional feature data, physical geographical resistance constraints composed of real topography are forcibly applied, dividing the geospatial structure map into multiple geographical unit sub-maps. This generates a spatially continuous, internally homogeneous, and strictly controlled spatial analysis base map, used for identifying mangrove ecological reserves, thereby improving the accuracy and efficiency of mangrove ecological reserve identification.

[0005] In a first aspect, embodiments of this application provide a method for constructing a distribution map of mangrove ecological reserves, comprising the following steps:

[0006] Obtain UAV multispectral imagery and elevation model data of the target area; perform multidimensional feature extraction based on the UAV multispectral imagery to obtain multidimensional feature data; Based on the multidimensional feature data and the elevation model data, the target area is divided into patches and subjected to topological analysis to obtain a geospatial structure map and edge set of the target area. The geospatial structure map includes several patches, and the edge set includes several connecting edges between adjacent patches. Based on the multidimensional feature data and the elevation model data, weights are assigned to the connecting edges between several adjacent patches in the edge set to construct an edge weight matrix. The edge weight matrix includes edge weight parameters of the connecting edges between several adjacent patches, and the edge weight parameters are used to reflect the physical and geographical resistance constraints between adjacent patches. Based on the edge weight matrix and the preset normalized cut model, the geospatial structure map is divided into several geographic unit sub-maps to construct a spatial analysis base map. Based on the multidimensional feature data of each geographic unit sub-map in the spatial analysis base map, mangrove community types are identified and mangrove ecological reserve distribution maps are constructed to obtain the distribution map of mangrove ecological reserves in the target area.

[0007] Secondly, embodiments of this application provide a device for constructing a distribution map of a mangrove ecological reserve, comprising: The data acquisition module is used to acquire UAV multispectral imagery and elevation model data of the target area; and to perform multidimensional feature extraction based on the UAV multispectral imagery to obtain multidimensional feature data. The topology analysis module is used to perform patch division and topology analysis on the target area based on the multidimensional feature data and the elevation model data, to obtain the geospatial structure map and edge set of the target area, wherein the geospatial structure map includes several patches; and the edge set includes several connecting edges between adjacent patches. The weight assignment module is used to assign weights to the connecting edges between several adjacent patches in the edge set according to the multidimensional feature data and the elevation model data, and construct an edge weight matrix, wherein the edge weight matrix includes edge weight parameters of the connecting edges between several adjacent patches. The subgraph partitioning module is used to divide the geospatial structure map into several geographic unit subgraphs based on the edge weight matrix and the preset normalized cut model, and to construct a spatial analysis base map. The distribution map construction module is used to identify mangrove community types and construct mangrove ecological reserve distribution maps based on the multidimensional feature data of each geographic unit sub-map in the spatial analysis base map, thereby obtaining the distribution map of mangrove ecological reserves in the target area.

[0008] Thirdly, embodiments of this application provide a computer device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, it implements the steps of the mangrove ecological reserve distribution map construction method as described in the first aspect.

[0009] Fourthly, embodiments of this application provide a storage medium storing a computer program, which, when executed by a processor, implements the steps of the mangrove ecological reserve distribution map construction method as described in the first aspect.

[0010] This application provides a method, apparatus, computer equipment, and storage medium for constructing a distribution map of mangrove ecological reserves. It utilizes multidimensional feature data constructed from UAV multispectral imagery and elevation model data to build a geospatial structure map and edge weight matrix for patch aggregation. Combining the edge weight matrix, a normalized cut model is introduced to find the globally optimal segmentation scheme. While considering multidimensional feature data, physical geographical resistance constraints composed of real topography are forcibly applied, dividing the geospatial structure map into multiple geographical unit sub-maps. This generates a spatially continuous, internally homogeneous, and strictly controlled spatial analysis base map, used for identifying mangrove ecological reserves, thereby improving the accuracy and efficiency of mangrove ecological reserve identification.

[0011] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0012] Figure 1 A flowchart illustrating a method for constructing a distribution map of a mangrove ecological reserve, as provided in one embodiment of this application; Figure 2 This is a flowchart illustrating step S2 of a method for constructing a distribution map of a mangrove ecological reserve provided in one embodiment of this application. Figure 3 This is a flowchart illustrating step S3 of a method for constructing a distribution map of a mangrove ecological reserve provided in one embodiment of this application. Figure 4 This is a flowchart illustrating step S31 of a method for constructing a distribution map of a mangrove ecological reserve provided in one embodiment of this application. Figure 5 This is a flowchart illustrating step S4 of a method for constructing a distribution map of a mangrove ecological reserve provided in one embodiment of this application. Figure 6 This is a flowchart illustrating step S5 of a method for constructing a distribution map of a mangrove ecological reserve provided in one embodiment of this application. Figure 7 This is a schematic diagram of the structure of a mangrove ecological reserve distribution map construction device provided in one embodiment of this application; Figure 8 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0013] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0014] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0015] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0016] Please see Figure 1 , Figure 1 The flowchart illustrates a method for constructing a distribution map of a mangrove ecological reserve, as provided in one embodiment of this application. The method includes the following steps: S1: Obtain UAV multispectral imagery and elevation model data of the target area; perform multidimensional feature extraction based on the UAV multispectral imagery to obtain multidimensional feature data.

[0017] The execution entity of the mangrove ecological reserve distribution map construction method is the construction equipment of the mangrove ecological reserve distribution map construction method (hereinafter referred to as the construction equipment). In an optional embodiment, the construction equipment can be a computer device, a server, or a server cluster composed of multiple computer devices.

[0018] In this embodiment, the construction device acquires UAV multispectral imagery and elevation model data of the target area. The construction device performs multidimensional feature extraction based on the UAV multispectral imagery to obtain multidimensional feature data, which includes spectral features, texture features, and vegetation ecological features. Each of the spectral features, texture features, and vegetation ecological features includes several corresponding feature vectors. Specifically, the spectral features include acquired red, green, red-edge, and near-infrared band features; the vegetation ecological features include the normalized vegetation index and leaf area index.

[0019] In an optional embodiment, the device performs radiometric calibration, atmospheric correction, orthorectification, and geometric registration on the UAV multispectral imagery and elevation model data to improve the accuracy of feature extraction.

[0020] S2: Based on the multidimensional feature data and the elevation model data, perform patch division and topological analysis on the target area to obtain the geospatial structure map and edge set of the target area.

[0021] In this embodiment, the construction device performs patch division and topological analysis on the target area based on the multidimensional feature data and the elevation model data to obtain a geospatial structure map and edge set of the target area. The geospatial structure map includes several patches, and the edge set includes several connecting edges between adjacent patches.

[0022] Please see Figure 2 , Figure 2 The flowchart of step S2 in the method for constructing a distribution map of a mangrove ecological reserve provided in one embodiment of this application includes steps S21 to S22, as follows: S21: Based on the multidimensional feature data and the elevation model data, the continuous raster pixels of the target area are divided into a set of minimum polygons, and the set of minimum polygons is used as a patch to obtain the geospatial structure map.

[0023] In this embodiment, the construction device divides the continuous raster pixels of the target area into a set of minimum polygons based on the multidimensional feature data and the elevation model data, and uses the set of minimum polygons as map features to obtain the geospatial structure map.

[0024] Specifically, the construction device unifies the spatial coordinate system and grid resolution of the geometrically registered multidimensional feature data and the elevation model data, and fuses them to form an initial feature space that accurately overlays multi-band spectra and surface elevations, constructing a multidimensional feature base map. The construction device uses a multi-resolution segmentation algorithm or a simple linear iterative clustering (SLIC) superpixel segmentation algorithm to integrate the spectral features and texture features in the multidimensional feature data and the elevation information provided by the elevation model data, and segments the continuous raster pixels of the target area into a set of minimal polygons with high internal homogeneity.

[0025] S22: Traverse each patch in the geospatial structure map, determine whether there is an intersection between each patch, take two patches that have an intersection as adjacent patches, construct connecting edges between adjacent patches, and obtain the edge set.

[0026] In this embodiment, the device traverses each patch in the geospatial structure map, determines whether there is an intersection between the patches, takes two patches that have an intersection as adjacent patches, constructs connecting edges between adjacent patches, and obtains the edge set. The geospatial structure map includes several patches; the edge set includes several connecting edges between adjacent patches.

[0027] Specifically, the device traverses each patch in the geospatial structure map, transforming the geospatial structure map into a graph theory model capable of mathematical calculations. The initial regional adjacency graph is defined as an undirected graph G = (V, E), where V is the set of nodes and E is the set of edges. The set of nodes includes the patch nodes corresponding to several patches, and the set of edges is constructed by extracting the spatial adjacency relationships between adjacent patches. If two patches share a boundary in space, it is determined that the two patches have an intersection. The two patches with an intersection are taken as adjacent patches, and a connecting edge is established between the corresponding two patch nodes to obtain the connecting edge between adjacent patches.

[0028] S3: Based on the multidimensional feature data and the elevation model data, assign weights to the connecting edges between several adjacent patches in the edge set to construct an edge weight matrix.

[0029] In this embodiment, the construction device assigns weights to the connecting edges between several adjacent patches in the edge set based on the multidimensional feature data and the elevation model data, and constructs an edge weight matrix. The edge weight matrix includes edge weight parameters of the connecting edges between several adjacent patches, and the edge weight parameters are used to reflect the physical and geographical resistance constraints between adjacent patches.

[0030] In an optional embodiment, the construction device sets the edge weight parameter of the connection edge between non-adjacent patches to 0.

[0031] Please see Figure 3 , Figure 3 The flowchart of step S3 in the method for constructing a distribution map of a mangrove ecological reserve provided in one embodiment of this application includes steps S31 to S32, as follows: S31: Based on the elevation model data, obtain the abrupt change values ​​of the relative elevation difference of the ground surface at the boundaries of each adjacent patch.

[0032] In this embodiment, the construction device obtains the abrupt change value of the relative elevation difference at the boundary of each adjacent patch based on the elevation model data, which serves as a geospatial resistance penalty term.

[0033] Please see Figure 4 , Figure 4 The flowchart of step S31 in the method for constructing a distribution map of a mangrove ecological reserve provided in an embodiment of this application is shown below, including steps S311 to S312: S311: Extract the common boundary pixel set of adjacent patches, and construct spatial buffers on both sides of the common boundary according to the common boundary pixel set and the preset pixel width to obtain a first spatial buffer and a second spatial buffer.

[0034] In this embodiment, the construction device extracts the common boundary pixel set of adjacent patches, and constructs spatial buffers on both sides of the common boundary according to the common boundary pixel set and the preset pixel width to obtain a first spatial buffer and a second spatial buffer.

[0035] S312: Based on the elevation model data, calculate the average elevation of the first spatial buffer and the second spatial buffer, and calculate the relative elevation change value of the ground surface based on the average elevation of the first spatial buffer and the second spatial buffer to obtain the relative elevation change value of the ground surface at the boundary of adjacent map patches.

[0036] In this embodiment, the construction device calculates the average elevation of the first spatial buffer and the second spatial buffer based on the elevation model data, and calculates the relative elevation difference abrupt change value of the ground surface based on the average elevation of the first spatial buffer and the second spatial buffer to obtain the relative elevation difference abrupt change value of the ground surface at the boundary of adjacent map patches, thereby accurately quantifying the blocking degree of the physical barrier.

[0037] S32: Based on the multidimensional feature data, the abrupt change values ​​of the relative elevation difference of the ground surface at the boundaries of each adjacent patch, and the preset edge weight parameter calculation algorithm, obtain the edge weight parameters of the connecting edges between each adjacent patch.

[0038] The algorithm for calculating the edge weight parameters is as follows:

[0039] In the formula, For the adjacent first i The first image patch and the first j The edge weight parameter of the connection edges between each patch. For the dual-constraint balance coefficient, The first of the multidimensional feature data k The weight parameters of each feature vector. For the first i In the multidimensional feature data of the map patch, the first k The values ​​of the eigenvectors, For the first j In the multidimensional feature data of the map patch, the first k The values ​​of the eigenvectors, To control the scaling parameter of similarity decay rate, For drag sensitivity coefficient, For the adjacent first i The first image patch and the first j Abrupt changes in relative elevation at the boundary of each map patch.

[0040] To overcome the shortcomings of traditional spatial clustering in overcoming real geographical barriers, in this embodiment, the construction device obtains the edge weight parameters of the connecting edges between adjacent patches based on the multidimensional feature data, the abrupt changes in relative surface elevation at the boundaries of each adjacent patch, and a preset edge weight parameter calculation algorithm. When adjacent patches cross deep ditches, cliffs, or tidal channels, The penalty term is significantly increased, and its value approaches 0, thus imposing a strong, rigid penalty on connecting edges that cross real physical terrain barriers, significantly reducing their overall connectivity weight. It is constrained by both "attribute similarity" and "geospatial resistance".

[0041] To ensure the engineering feasibility of the above dual-constraint model, the specific methods for obtaining each parameter in the formula and the optimal range are set as follows: the dual-constraint balance coefficient The preferred value range is [0.4, 0.6]; the first value in the multidimensional feature data is... k Weight parameters of each feature vector The scale parameter controlling similarity decay is pre-calculated based on the degree of dispersion of the corresponding feature vectors using the information entropy weighting method. and drag sensitivity coefficient This is an adaptive normalization setting based on the average area of ​​the initial subdivided patches and the difference in surface elevation input from the target area.

[0042] S4: Based on the edge weight matrix and the preset normalized cut model, the geospatial structure map is divided into several geographic unit sub-maps to construct a spatial analysis base map.

[0043] In this embodiment, the construction device divides the geospatial structure map into several geographic unit sub-maps based on the edge weight matrix and the preset normalized cut model, constructs a spatial analysis base map, and transforms the complex geographic patch aggregation problem into the optimal segmentation problem of the global map. It outputs a refined set of geographic units with the highest internal homogeneity and strictly controlled by natural geographic barriers on a global scale.

[0044] In an optional embodiment, the construction device uses a GIS vector smoothing algorithm to perform tolerance smoothing on the boundaries of several geographic unit sub-maps, eliminating harsh polylines and burrs. Subsequently, topology rule checks are performed to eliminate minor overlaps and gaps between polygons, generating a seamlessly stitched spatial analysis base map with smooth boundaries that conforms to the natural terrain.

[0045] Please see Figure 5 , Figure 5 The flowchart of step S4 in the method for constructing a distribution map of a mangrove ecological reserve provided in one embodiment of this application includes steps S41 to S43, as follows: S41: Using the geospatial structure map as the input map, construct the angle matrix based on the edge weight matrix of the input map to obtain the angle matrix; solve the equation based on the edge weight matrix, the angle matrix, and the preset generalized eigenvalue equation to obtain several sets of solution results.

[0046] In this embodiment, the construction device uses the geospatial structure map as the input map and constructs the angle matrix based on the edge weight matrix of the input map to obtain the angle matrix.

[0047] The construction device solves the edge weight matrix, the angle matrix, and the preset generalized eigenvalue equation to obtain several sets of solution results. These results include generalized eigenvalues ​​and corresponding generalized eigenvectors. The generalized eigenvalues, in a physical sense, represent the "normalized cut energy cost" consumed during each graph cut. A smaller generalized eigenvalue indicates greater geographical resistance at the cut, meaning it better conforms to a natural boundary. The generalized eigenvectors include component values ​​corresponding to several map features. The generalized eigenvalue equation is as follows:

[0048] In the formula, For the angle matrix, This is the edge weight matrix. For generalized eigenvectors, These are generalized eigenvalues.

[0049] S42: Based on the generalized feature values ​​in several sets of solution results, extract the target solution result from several sets of solution results, and divide the geographic units according to the component values ​​corresponding to each patch in the generalized feature vector of the target solution result and the preset division threshold to obtain the first initial geographic unit sub-map and the second initial geographic unit sub-map.

[0050] In this embodiment, the construction device extracts the target solution result from several sets of solution results based on the generalized eigenvalues ​​in the solution results. Specifically, the construction device extracts the solution result of the second smallest generalized eigenvalue as the target solution result.

[0051] The construction device divides geographic units based on the component values ​​corresponding to each patch in the generalized feature vector of the target solution and a preset division threshold, obtaining a first initial geographic unit sub-map and a second initial geographic unit sub-map. Specifically, if the component value is greater than the division threshold, the patch corresponding to the component value is assigned to the first initial geographic unit sub-map; if the component value is less than or equal to the division threshold, the patch corresponding to the component value is assigned to the second initial geographic unit sub-map.

[0052] S43: Based on the first initial geographic unit subgraph, the second initial geographic unit subgraph, the edge weight matrix, and the preset normalized cut objective function, normalized cut values ​​are calculated. It is determined whether the normalized cut values ​​obtained meet the preset upper limit of normalized cut values. If not, the first initial geographic unit subgraph and the second initial geographic unit subgraph are used as input graphs respectively, and the geographic unit subgraph partitioning and normalized cut value calculation are repeated until the normalized cut values ​​obtained meet the upper limit of normalized cut values, thereby obtaining several geographic unit subgraphs.

[0053] In this embodiment, the construction device calculates the normalized cut value based on the first initial geographic unit subgraph, the second initial geographic unit subgraph, the edge weight matrix, and a preset normalized cut objective function. The preset normalized cut objective function is:

[0054] In the formula, For normalized cut values, This is the first initial geographic unit sub-map. This is the second initial geographic unit sub-map. For the input image, , These represent the submaps assigned to the first and second initial geographic units, respectively. and For adjacent patches, Map features divided into the first initial geographic unit submap Map features that are adjacent to the second initial geographic unit submap The edge weight parameter of the connection edge between them. This represents the polygon in the input image. Map features divided into the first initial geographic unit submap Patches of adjacent input graphs The edge weight parameter of the connection edge between them. Map features divided into the second initial geographic unit submap Patches of adjacent input graphs The edge weight parameter of the connecting edges between .

[0055] The system determines whether the normalized cut value obtained from the solution meets the preset upper limit of the normalized cut value. If not, it uses the first initial geographic unit subgraph and the second initial geographic unit subgraph as input graphs, and repeats the geographic unit subgraph partitioning and normalized cut value solution until the obtained normalized cut value meets the upper limit of the normalized cut value, thus obtaining several geographic unit subgraphs. A multi-level recursive bisection strategy is adopted. That is, each solution focuses on efficiently and accurately partitioning the current graph structure into two disjoint subsets; then, through recursive loops, a hierarchical subdivision from coarse to fine is achieved, thereby ensuring global computational efficiency and boundary optimality while ultimately achieving a fine partition of the geographic unit subgraphs.

[0056] In an optional embodiment, after dividing the geographic unit sub-map, the construction device calculates the unit area of ​​the obtained first initial geographic unit sub-map and second initial geographic unit sub-map respectively. If the calculated unit area is less than the preset unit area threshold, the normalized cut value solution is stopped, and several geographic unit sub-maps are obtained.

[0057] S5: Based on the multidimensional feature data of each geographic unit sub-map in the spatial analysis base map, identify mangrove community types and construct a distribution map of mangrove ecological reserves to obtain a distribution map of mangrove ecological reserves in the target area.

[0058] In this embodiment, the construction device identifies mangrove community types and constructs a distribution map of mangrove ecological reserves based on the multidimensional feature data of each geographic unit sub-map in the spatial analysis base map, thereby obtaining a distribution map of mangrove ecological reserves in the target area.

[0059] Please see Figure 6 , Figure 6 The flowchart of step S5 in the method for constructing a distribution map of a mangrove ecological reserve provided in one embodiment of this application includes steps S51 to S52, as follows: S51: Perform joint feature calculation based on the multidimensional feature data of each geographic unit sub-map to obtain the joint feature vector of each geographic unit sub-map; input the joint feature vector of each geographic unit sub-map into the pre-trained mangrove community classification model for pattern recognition to obtain the mangrove community type of each geographic unit sub-map.

[0060] In this embodiment, the construction device performs joint feature calculation based on the multidimensional feature data of each geographic unit sub-map to obtain the joint feature vector of each geographic unit sub-map. Specifically, the construction device statistically calculates and calculates the average spectral features and average texture features of all pixels within each geographic unit sub-map based on the multidimensional feature data of each geographic unit sub-map to obtain the joint feature vector of each geographic unit sub-map.

[0061] The device inputs the joint feature vectors of each geographic unit sub-map into a pre-trained mangrove community classification model for pattern recognition to obtain the mangrove community type of each geographic unit sub-map, wherein the mangrove community type includes Kandelia candel community, tung tree community and white mangrove community.

[0062] S52: Based on the mangrove community type of each geographic unit submap, merge the geographic unit submaps with the same mangrove community type and adjacent locations to construct a distribution map of mangrove ecological protection zones in the target area.

[0063] The device merges mangrove community types from various geographic unit sub-maps that are adjacent to each other and have the same mangrove community type to construct a distribution map of mangrove ecological protection zones in the target area.

[0064] Specifically, at the spatial level, the construction device performs vector fusion (Dissolve) processing on the sub-maps of various geographical units with the same mangrove community type and adjacent locations, eliminating internal shared boundaries to extract contiguous and continuous physical boundaries of mangrove communities. The device then performs spatial topological relationship checks on the extracted physical boundaries of various mangrove communities, fills small patch gaps, and smooths the boundaries according to the principle of ecological connectivity. The optimized physical boundaries of various mangrove communities are then layered and spatially synthesized to finally generate a distribution map of mangrove ecological protection zones with a complete structure and boundaries that conform to the real natural landform barriers.

[0065] Please refer to Figure 7 , Figure 7 This is a schematic diagram of a mangrove ecological reserve distribution map construction device provided in one embodiment of this application. The device can be implemented entirely or partially through software, hardware, or a combination of both. The device 7 includes: The data acquisition module 71 is used to acquire UAV multispectral imagery and elevation model data of the target area; and to perform multidimensional feature extraction based on the UAV multispectral imagery to obtain multidimensional feature data. The topology analysis module 72 is used to perform patch division and topology analysis on the target area based on the multidimensional feature data and the elevation model data, to obtain a geospatial structure map and edge set of the target area, wherein the geospatial structure map includes a number of patches; and the edge set includes a number of connecting edges between adjacent patches. The weight assignment module 73 is used to assign weights to the connecting edges between several adjacent patches in the edge set according to the multidimensional feature data and the elevation model data, and construct an edge weight matrix. The edge weight matrix includes edge weight parameters of the connecting edges between several adjacent patches, and the edge weight parameters are used to reflect the physical and geographical resistance constraints between adjacent patches. The subgraph partitioning module 74 is used to divide the geospatial structure map into several geographic unit subgraphs according to the edge weight matrix and the preset normalized cut model, and to construct a spatial analysis base map. The distribution map construction module 75 is used to identify mangrove community types and construct mangrove ecological reserve distribution maps based on the multidimensional feature data of each geographic unit sub-map in the spatial analysis base map, so as to obtain the distribution map of mangrove ecological reserve in the target area.

[0066] In this embodiment, a data acquisition module obtains UAV multispectral imagery and elevation model data of the target area; multidimensional feature extraction is performed on the UAV multispectral imagery to obtain multidimensional feature data; a topology analysis module performs patch division and topology analysis on the target area based on the multidimensional feature data and the elevation model data to obtain a geospatial structure map and edge set of the target area, wherein the geospatial structure map includes several patches; the edge set includes several connecting edges between adjacent patches; and a weight assignment module assigns weights to several adjacent patches in the edge set based on the multidimensional feature data and the elevation model data. Weights are assigned to the connecting edges between map features to construct an edge weight matrix. This matrix includes edge weight parameters for connecting edges between several adjacent map features, reflecting the physical and geographical resistance constraints between them. A sub-map partitioning module divides the geospatial structure map into several geographic unit sub-maps based on the edge weight matrix and a preset normalized cut model, constructing a spatial analysis base map. A distribution map construction module identifies mangrove community types and constructs a distribution map of mangrove ecological reserves based on the multidimensional feature data of each geographic unit sub-map in the spatial analysis base map, obtaining a distribution map of mangrove ecological reserves for the target area. By utilizing multidimensional feature data and elevation model data constructed from UAV multispectral imagery, a geospatial structure map and edge weight matrix are built for patch aggregation. Combined with the edge weight matrix, a normalized cut model is introduced to find the globally optimal segmentation scheme. While considering multidimensional feature data, physical geographical resistance constraints composed of real topography are forcibly applied, dividing the geospatial structure map into multiple geographical unit sub-maps. This generates a spatially continuous, internally homogeneous, and strictly controlled spatial analysis base map, which is used for the identification of mangrove ecological reserves, thereby improving the accuracy and efficiency of mangrove ecological reserve identification.

[0067] Please refer to Figure 8 , Figure 8 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. The computer device 8 includes: a processor 81, a memory 82, and a computer program 83 stored in the memory 82 and executable on the processor 81. The computer device can store multiple instructions, which are adapted to be loaded and executed by the processor 81 as described above. Figures 1 to 6 The method steps of the illustrated embodiment, and the specific execution process, can be found in the illustration. Figures 1 to 6 The specific details of the illustrated embodiments will not be elaborated here.

[0068] The processor 81 may include one or more processing cores. The processor 81 connects to various parts of the server using various interfaces and lines, and executes various functions and processes data of the mangrove ecological reserve distribution map construction device 7 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 82, and by calling data stored in the memory 82. Optionally, the processor 81 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 81 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU mainly handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required to be displayed on the touch screen; and the modem is used for wireless communication. It is understood that the modem may also not be integrated into the processor 81 and may be implemented as a separate chip.

[0069] The memory 82 may include random access memory (RAM) or read-only memory. Optionally, the memory 82 may include a non-transitory computer-readable storage medium. The memory 82 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 82 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 82 may also be at least one storage device located remotely from the aforementioned processor 81.

[0070] This application embodiment also provides a storage medium that can store multiple instructions, which are adapted to be loaded and executed by a processor as described above. Figures 1 to 6 The method steps of the illustrated embodiment, and the specific execution process, can be found in the illustration. Figures 1 to 6 The specific details of the illustrated embodiments will not be elaborated here.

[0071] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0072] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0073] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the algorithm. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0074] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms.

[0078] This invention is not limited to the above-described embodiments. If any modifications or variations to this invention do not depart from the spirit and scope of this invention, and if such modifications and variations fall within the scope of the claims and equivalent technologies of this invention, then this invention also intends to include such modifications and variations.

Claims

1. A method for constructing a distribution map of mangrove ecological reserves, characterized in that, Includes the following steps: Obtain UAV multispectral imagery and elevation model data of the target area; perform multidimensional feature extraction based on the UAV multispectral imagery to obtain multidimensional feature data; Based on the multidimensional feature data and the elevation model data, the target area is divided into patches and subjected to topological analysis to obtain a geospatial structure map and edge set of the target area. The geospatial structure map includes several patches, and the edge set includes several connecting edges between adjacent patches. Based on the multidimensional feature data and the elevation model data, weights are assigned to the connecting edges between several adjacent patches in the edge set to construct an edge weight matrix. The edge weight matrix includes edge weight parameters of the connecting edges between several adjacent patches, and the edge weight parameters are used to reflect the physical and geographical resistance constraints between adjacent patches. Based on the edge weight matrix and the preset normalized cut model, the geospatial structure map is divided into several geographic unit sub-maps to construct a spatial analysis base map. Based on the multidimensional feature data of each geographic unit sub-map in the spatial analysis base map, mangrove community types are identified and mangrove ecological reserve distribution maps are constructed to obtain the distribution map of mangrove ecological reserves in the target area.

2. The method for constructing a distribution map of mangrove ecological reserves according to claim 1, characterized in that, The step of dividing the target area into patches and determining connectivity based on the multidimensional feature data and the elevation model data to obtain the geospatial structure map and edge set of the target area includes the following steps: Based on the multidimensional feature data and the elevation model data, the continuous raster pixels of the target area are divided into a set of minimum polygons, and the set of minimum polygons is used as a patch to obtain the geospatial structure map. Traverse all the patches in the geospatial structure map, determine whether there is an intersection between the patches, take the two patches that have an intersection as adjacent patches, construct the connecting edges between the adjacent patches, and obtain the edge set.

3. The method for constructing a distribution map of mangrove ecological reserves according to claim 2, characterized in that, The step of assigning weights to the connecting edges between several adjacent patches in the edge set based on the multidimensional feature data and the elevation model data, and constructing an edge weight matrix, includes the following steps: Based on the elevation model data, the abrupt change values ​​of the relative elevation difference of the ground surface at the boundaries of each adjacent patch are obtained; Based on the multidimensional feature data, the abrupt changes in relative elevation at the boundaries of each adjacent patch, and a preset edge weight parameter calculation algorithm, the edge weight parameters of the connecting edges between each adjacent patch are obtained. The edge weight parameter calculation algorithm is as follows: In the formula, For the adjacent first i The first image patch and the first j The edge weight parameter of the connection edges between each patch. For the double-constraint balance coefficient, The first of the multidimensional feature data k The weight parameters of each feature vector. For the first i In the multidimensional feature data of the map patch, the first k The values ​​of the feature vectors, For the first j In the multidimensional feature data of the map patch, the first k The values ​​of the feature vectors, To control the scaling parameter of similarity decay rate, For drag sensitivity coefficient, For the adjacent first i The first image patch and the first j Abrupt changes in relative elevation at the boundary of each map patch.

4. The method for constructing a distribution map of mangrove ecological reserves according to claim 3, characterized in that, The step of obtaining the abrupt change in relative elevation difference at the boundaries of each adjacent patch based on the elevation model data includes the following steps: Extract the common boundary pixel set of adjacent patches, and construct spatial buffers on both sides of the common boundary according to the common boundary pixel set and the preset pixel width to obtain the first spatial buffer and the second spatial buffer. Based on the elevation model data, the average elevation values ​​of the first spatial buffer and the second spatial buffer are calculated. Based on the average elevation values ​​of the first spatial buffer and the second spatial buffer, the abrupt change values ​​of the relative elevation difference of the ground surface are calculated to obtain the abrupt change values ​​of the relative elevation difference of the ground surface at the boundary of adjacent map patches.

5. The method for constructing a distribution map of mangrove ecological reserves according to claim 1 or 4, characterized in that, The step of dividing the geospatial structure map into several geographic unit sub-maps based on the edge weight matrix and a preset normalized cut model to construct a spatial analysis base map includes the following steps: Using the geospatial structure map as input, an angle matrix is ​​constructed based on the edge weight matrix of the input map to obtain the angle matrix. The angle matrix is ​​then solved using the edge weight matrix, the angle matrix, and a pre-defined generalized eigenvalue equation to obtain several sets of solution results. These solution results include generalized eigenvalues ​​and corresponding generalized eigenvectors. The generalized eigenvectors include component values ​​corresponding to several map features. The generalized eigenvalue equation is as follows: In the formula, For the angle matrix, This is the edge weight matrix. For generalized eigenvectors, These are generalized eigenvalues; Based on the generalized feature values ​​in several sets of solution results, the target solution result is extracted from the several sets of solution results. Geographic units are divided according to the component values ​​corresponding to each patch in the generalized feature vector of the target solution result and the preset division threshold, so as to obtain the first initial geographic unit sub-map and the second initial geographic unit sub-map. Based on the first initial geographic unit subgraph, the second initial geographic unit subgraph, the edge weight matrix, and a preset normalized cut objective function, normalized cut values ​​are calculated. It is then determined whether the obtained normalized cut values ​​meet a preset upper limit for normalized cut values. If not, the first and second initial geographic unit subgraphs are used as input graphs, and the geographic unit subgraph partitioning and normalized cut value calculation are repeated until the obtained normalized cut values ​​meet the upper limit, resulting in several geographic unit subgraphs. The normalized cut objective function is: In the formula, For normalized cut values, This is the first initial geographic unit sub-map. This is the second initial geographic unit sub-map. For the input image, , These represent the submaps assigned to the first and second initial geographic units, respectively. and For adjacent patches, Map pieces divided into the first initial geographic unit submap Map features that are adjacent to the second initial geographic unit submap The edge weight parameter of the connection edge between them. This represents the polygon in the input image. Map pieces divided into the first initial geographic unit submap Patches of adjacent input graphs The edge weight parameter of the connection edge between them. Map features divided into the second initial geographic unit submap Patches of adjacent input graphs The edge weight parameter of the connecting edges between .

6. The method for constructing a distribution map of mangrove ecological reserves according to claim 5, characterized in that, The process of identifying mangrove community types and constructing a distribution map of mangrove ecological reserves based on the multidimensional feature data of each geographic unit sub-map in the spatial analysis base map, to obtain a distribution map of mangrove ecological reserves in the target area, includes the following steps: The joint feature vector of each geographic unit sub-map is obtained by performing joint feature calculation based on the multidimensional feature data of each geographic unit sub-map; the joint feature vector of each geographic unit sub-map is then input into a pre-trained mangrove community classification model for pattern recognition to obtain the mangrove community type of each geographic unit sub-map. Based on the mangrove community types of each geographic unit submap, geographic unit submaps with the same mangrove community type and adjacent locations are merged to construct a distribution map of mangrove ecological protection zones in the target area.

7. A device for constructing a distribution map of mangrove ecological reserves, characterized in that, include: The data acquisition module is used to acquire UAV multispectral imagery and elevation model data of the target area. Multidimensional feature extraction is performed on the UAV multispectral image to obtain multidimensional feature data; The topology analysis module is used to perform patch division and topology analysis on the target area based on the multidimensional feature data and the elevation model data, to obtain the geospatial structure map and edge set of the target area, wherein the geospatial structure map includes several patches; and the edge set includes several connecting edges between adjacent patches. The weight assignment module is used to assign weights to the connecting edges between several adjacent patches in the edge set according to the multidimensional feature data and the elevation model data, and construct an edge weight matrix. The edge weight matrix includes edge weight parameters of the connecting edges between several adjacent patches, and the edge weight parameters are used to reflect the physical and geographical resistance constraints between adjacent patches. The subgraph partitioning module is used to divide the geospatial structure map into several geographic unit subgraphs based on the edge weight matrix and the preset normalized cut model, and to construct a spatial analysis base map. The distribution map construction module is used to identify mangrove community types and construct mangrove ecological reserve distribution maps based on the multidimensional feature data of each geographic unit sub-map in the spatial analysis base map, thereby obtaining the distribution map of mangrove ecological reserves in the target area.

8. A computer device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor; when executed by the processor, the computer program implements the steps of the method for constructing a distribution map of a mangrove ecological reserve as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for constructing a distribution map of mangrove ecological protection zones as described in any one of claims 1 to 6.

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