Intelligent urban ecological corridor construction method based on multi-source surveying and mapping data driving

Abstract

The invention relates to the technical field of ecological corridor construction, in particular to an intelligent urban ecological corridor construction method based on multi-source surveying and mapping data driving. The method comprises the following steps: acquiring a satellite remote sensing image and terrain elevation data, integrating the data into multi-source surveying and mapping data, identifying space-time reference features of each data source, extracting ground feature types and vegetation coverage features, constructing a space-time registration relationship of the data according to the space-time reference features, associating the ground feature types with the vegetation features, and establishing a multi-source surveying and mapping result. According to the method, ecological source candidate areas are screened, greening connected obstacles in the ecological source are identified, candidate ecological corridor paths are identified in combination with preset ecological connectivity evaluation data, ecological node areas are reconstructed, and an urban ecological corridor network structure is generated based on the nodes and the candidate ecological corridor paths. According to the method, more efficient and more accurate ecological characteristic analysis is realized, the connectivity and effectiveness of the ecological corridor network are ensured, and the overall stability of the ecological network is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological corridor construction, and in particular to an intelligent construction method of urban ecological corridor based on multi-source surveying and mapping data. BACKGROUND

[0002] Traditional ecological corridor construction methods often rely on a single data source, lack comprehensive analysis of the environment, and cannot accurately identify ecological characteristics and potential ecological sources. The existing technology has obvious shortcomings in data integration, and fails to effectively utilize the advantages of satellite remote sensing images and terrain elevation data, resulting in low accuracy and reliability of ecological feature extraction. In particular, in complex urban environments, traditional methods are difficult to cope with diverse land cover types and vegetation cover conditions, limiting the scientific planning of ecological corridors. The existing methods are outdated in terms of temporal and spatial registration and feature correlation, and cannot effectively establish the spatio-temporal relationship between multi-source surveying and mapping data, resulting in inaccurate selection of ecological source candidate areas and lack of in-depth analysis of ecological connectivity barriers, which affects the rationality and effectiveness of ecological corridor paths. Overall, the connectivity and functionality of urban ecological systems have not been fully realized. SUMMARY

[0003] Therefore, it is necessary to provide an intelligent construction method of urban ecological corridor based on multi-source surveying and mapping data to solve at least one of the above technical problems.

[0004] To achieve the above purpose, an intelligent construction method of urban ecological corridor based on multi-source surveying and mapping data includes the following steps: Step S1: Obtain satellite remote sensing images and terrain elevation data and integrate them into multi-source surveying and mapping data; identify the spatio-temporal reference features of each data source in the multi-source surveying and mapping data; extract the land feature characteristics and vegetation cover characteristics in the multi-source surveying and mapping data; Step S2: Construct the spatio-temporal registration relationship of multi-source surveying and mapping data according to the spatio-temporal reference features; correlate the land feature characteristics and vegetation cover characteristics based on the spatio-temporal registration relationship, and select the ecological source candidate area; Step S3: Identify the green connectivity barrier based on the ecological source candidate area; and identify the candidate ecological corridor path based on the pre-set ecological connectivity evaluation data and the green connectivity barrier; Step S4: Reconstruct the ecological node area with spatial correlation in the ecological source candidate area, and generate the urban ecological corridor network structure based on the ecological node area and the candidate ecological corridor path.

[0005] The present application integrates satellite remote sensing images and terrain elevation data into multi-source surveying and mapping data, ensures comprehensive monitoring and analysis of urban ecological environment, identifies the time and space reference characteristics of each data source, provides a solid foundation for subsequent data registration, extracts ground feature characteristics and vegetation cover characteristics, makes the analysis of ecological characteristics more accurate, provides an important basis for candidate area screening of ecological sources, comprehensively utilizes the advantages of multi-source data, and significantly improves the timeliness and accuracy of data.

[0006] In the process of constructing the spatio-temporal registration relationship, based on the correlation of spatio-temporal characteristics, the ground feature characteristics and the vegetation cover characteristics can be effectively associated, and the candidate areas meeting the ecological requirements can be screened out, the recognition rate of the ecological source area is improved, based on the identification of the green connection obstacle body of the ecological source area candidate area, combined with the preset ecological connectivity evaluation data, the potential ecological corridor path can be accurately identified, the connectivity and effectiveness of the ecological corridor network are ensured, and the overall stability of the ecological network is enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 It is a step flowchart of a kind of urban ecological corridor intelligent construction method based on multi-source surveying and mapping data driving; Figure 2 It is Figure 1 It is a detailed implementation step flowchart of step S3; Figure 3 It is a schematic diagram of ecological source area connection direction.

[0008] The implementation of the present application, functional characteristics and advantages will be further described with reference to the embodiments and accompanying drawings. DETAILED DESCRIPTION

[0009] The technical method of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0010] In addition, the accompanying drawings are only schematic illustrations of the present application, and are not necessarily drawn to scale. The same reference signs in the drawings represent the same or similar parts, so repeated description thereof will be omitted. Some block diagrams shown in the drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0011] It should be understood that, although the terms "first", "second" or the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element without departing from the scope of the example embodiments. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0012] To achieve the above object, please refer to Figures 1 to 3 A method for intelligent construction of urban ecological corridors based on multi-source surveying and mapping data driving, comprising the following steps: Step S1: Obtain satellite remote sensing images and terrain elevation data and integrate them into multi-source surveying and mapping data; identify the spatio-temporal reference features of each data source in the multi-source surveying and mapping data; extract the feature of ground object types and the feature of vegetation coverage in the multi-source surveying and mapping data; Step S2: Construct the spatio-temporal registration relationship of the multi-source surveying and mapping data according to the spatio-temporal reference features; associate the feature of ground object types and the feature of vegetation coverage with the spatio-temporal registration relationship, and screen the candidate areas of ecological sources; Step S3: Identify the greenery connectivity obstacles based on the candidate areas of ecological sources; identify the candidate ecological corridor paths through the pre-set ecological connectivity evaluation data and the greenery connectivity obstacles; Step S4: Reconstruct the ecological node areas with spatial correlation in the candidate areas of ecological sources, and generate the network structure of urban ecological corridors based on the ecological node areas and the candidate ecological corridor paths.

[0013] In this embodiment, through the joint collection of high-resolution satellite image data sources and terrain elevation digital elevation model (DEM) data sources, a multi-source data set containing spatial coordinates, surface reflectivity, slope and slope direction information is constructed, and through spatial rasterization processing, each data source is aligned under the same geographic coordinate system, thereby forming multi-source surveying and mapping data. It should be noted that, when integrating, a matching strategy based on coordinate consistency and time synchronization should be adopted to avoid spatio-temporal differences causing ground object recognition errors. Specifically, in the data integration process, the time sequence difference value is calculated through image timestamp comparison and terrain projection transformation, and the time sequence difference value is controlled within 30 seconds, thereby realizing high-precision synchronous fusion of satellite and terrain data. Further, after the multi-source data fusion is completed, a double-feature extraction method based on spectral index (such as NDVI normalized vegetation index) and texture direction matrix analysis is adopted to form the ground object classification feature set and the vegetation coverage density matrix when extracting the feature of ground object types and the feature of vegetation coverage.

[0014] The spatial reference system (such as WGS84 or CGCS2000) and image acquisition time of each data source are extracted, a space-time registration matrix is constructed, and the pixel positions of each image are accurately aligned under the unified time reference. It needs to be explained that the space-time registration relationship not only includes spatial position matching, but also includes time level synchronization correction to ensure that the change analysis of ecological characteristics has time consistency. Specifically, the linear time interpolation is used to interpolate the ground feature images at different times, so that the pixel difference in adjacent time periods is less than 1%. Then, the terrain elevation data and satellite reflection data are spatially reprojected by the registration matrix. Further, after the registration is completed, the ground feature type characteristics are associated with the vegetation cover characteristics, and the ecological source candidate area is screened through the ecological sensitivity calculation based on the threshold partition. The ecological connectivity index and the vegetation integrity are used as the judgment conditions to generate the ecological source candidate raster set.

[0015] The green connectivity barrier is identified based on the ecological source candidate area, and the candidate ecological corridor path is identified through the preset ecological connectivity evaluation data. It needs to be explained that the green connectivity barrier refers to the artificial construction body or natural fragmentation zone that affects the continuity of the ecological network. In the identification process, the barrier body is extracted through spatial segmentation and ecological resistance matrix calculation. Specifically, the ecological source candidate area is superimposed with the road network, building distribution layer and bare land block data, and the ecological resistance value of each barrier body is calculated. When the resistance value is more than twice the average resistance of the region, it is marked as a green connectivity barrier. Further, the ecological flow path is calculated through the preset ecological connectivity evaluation data. According to the principle of minimum cumulative resistance, multiple candidate ecological corridor paths are generated from the ecological source to the target area. According to the average vegetation coverage and water system continuity of the path, the weighted score is calculated, and finally the candidate ecological corridor path set with the best connectivity is selected.

[0016] The ecological node area with spatial correlation in the ecological source candidate area is reconstructed, and the urban ecological corridor network structure is generated based on the ecological node area and the candidate ecological corridor path. It needs to be explained that the ecological node area is a key area unit in the ecological source that has spatial ecological function connection. Its identification depends on the ecological structure aggregation degree analysis. Specifically, first, the spatial autocorrelation coefficient of each grid cell in the ecological source candidate area is calculated, and the high correlation area set obtained by threshold segmentation is used as the initial node. Then, the spatial adjacency matrix between nodes is established through regional topological relationship analysis. Further, when generating the ecological corridor network structure, the shortest ecological path between nodes is used as the connection edge, and the ecological node is used as the network vertex to form the initial ecological network. According to the network degree centrality and connectivity optimization algorithm, the path level structure is adjusted to make the main corridor and branch corridor present continuous and non-overlapping distribution in spatial layout, so as to form a complete urban ecological corridor network structure.

[0017] Preferably, the step S2 of constructing the spatio-temporal registration relationship of the multi-source surveying and mapping data according to the spatio-temporal reference feature is specifically: According to the spatio-temporal reference feature, the spatial coverage accuracy corresponding to each data source in the multi-source surveying and mapping data is reconstructed; The data fusion weight coefficient is calculated through the spatial coverage accuracy, and the spatio-temporal reference feature is corrected by using the data fusion weight coefficient, so as to generate the standardized surveying and mapping parameter; The spatio-temporal corresponding relationship between the spatial coverage accuracy and the standardized surveying and mapping parameter is established, and the spatio-temporal registration relationship of the multi-source surveying and mapping data is constructed based on the spatio-temporal corresponding relationship between the spatial coverage accuracy and the standardized surveying and mapping parameter.

[0018] In this embodiment, the spatio-temporal registration relationship of the multi-source surveying and mapping data is constructed according to the spatio-temporal reference feature, which involves the consistency processing of the multi-source surveying and mapping data in the time dimension and the space dimension. Through systematic correction of the spatial resolution, the time sampling period and the coordinate reference of different observation sources, it is ensured that all kinds of data have a unified geographic reference framework in the registration process.

[0019] Specifically, the spatial coverage accuracy of each data source in the multi-source surveying and mapping data is reconstructed according to the spatio-temporal reference feature, which includes numerical evaluation of the pixel resolution accuracy of satellite remote sensing image, the sampling interval accuracy of terrain elevation data and the geographic positioning error of ground object image, and stores it as a spatial coverage accuracy matrix data. Through the matrix, the coverage ability and error range of each data source in the spatial domain can be quantified.

[0020] Further, the fusion weight coefficient of each data source is calculated by performing normalization processing on the spatial coverage accuracy matrix data, and the spatio-temporal reference feature is weighted and corrected by using the fusion weight coefficient. In this process, the weight balance parameter And is set to control the trade-off ratio of spatial accuracy and temporal accuracy, so that the weighted spatio-temporal feature has standardized consistency characteristics, thereby generating a standardized surveying and mapping parameter set data.

[0021] It needs to be explained that the spatio-temporal corresponding relationship between the spatial coverage accuracy and the standardized surveying and mapping parameter is established, the spatial coordinate reference point and the time sampling node are jointly indexed to form a spatio-temporal mapping table, and finally the spatio-temporal registration relationship of the multi-source surveying and mapping data is constructed based on the spatio-temporal mapping table and output as a standardized registration data structure.

[0022] Especially important is that the spatio-temporal registration relationship is used to associate the ground object type feature and the vegetation coverage feature, and to screen the ecological source land candidate area, which is specifically: The ground object type feature and the vegetation coverage feature are superimposed at the pixel level based on the spatio-temporal registration relationship to form a ground object-vegetation registration data set; The average vegetation coverage index of each feature type area is calculated through the feature-vegetation registration dataset, and the area with stable and continuous vegetation coverage is identified; The ecological continuous unit is constructed according to the area with stable and continuous vegetation coverage; The ecological source candidate area is screened based on the ecological continuous unit.

[0023] In this embodiment, the feature type characteristics and the vegetation coverage characteristics are associated in the spatio-temporal registration relationship, and the ecological source candidate area is screened, and through the spatial and temporal two-dimensional registration of multi-source surveying and mapping data, the high-precision fusion of feature information and vegetation information is realized, so as to extract the area with ecological continuity and vegetation integrity Specifically, the feature type characteristics and the vegetation coverage characteristics are superimposed at the pixel level based on the spatio-temporal registration relationship, the registered feature type grid and the vegetation index (NDVI, Normalized Difference Vegetation Index) grid are spatially aligned, each pixel unit has feature type attribute and vegetation coverage value, and a feature-vegetation registration dataset is formed, which records the coupling relationship between feature distribution and vegetation state in a spatial index manner.

[0024] Further, the average vegetation coverage index of each feature type area is calculated through the feature-vegetation registration dataset, the sliding window method is used for block calculation of the urban land surface, the window size can be set to 50m*50m to ensure the smoothness of the area statistics, and according to the calculation result, the area with high value and small change amplitude of the vegetation coverage index in time sequence is identified as the area with stable and continuous vegetation coverage.

[0025] It should be explained that the ecological continuous unit is constructed according to the spatial connectivity, whether the adjacent pixels belong to the same continuous structure is judged by analyzing the connection direction and distance threshold, and finally the region screening is carried out based on the formed ecological continuous unit, the units with small area or adjacent to high interference area are removed, and the remaining units meeting the connectivity and coverage conditions are marked as ecological source candidate areas.

[0026] Preferably, step S3 is specifically: Step S31: identifying the green connection obstacle based on the vegetation coverage characteristics of the ecological source candidate area and the terrain elevation data; Step S32: performing spatial superposition analysis on the green connection obstacle and the preset ecological connectivity evaluation data, and calculating the corresponding connection obstacle response data of each grid unit, so as to deduce the ecological connectivity differentiation characteristics; Step S33: analyzing the spatial distribution of the candidate ecological corridor path according to the ecological connectivity differentiation characteristics, so as to identify the candidate ecological corridor path.

[0027] In this embodiment, a preliminary ecological corridor spatial framework is constructed based on candidate ecological source areas. By overlaying and analyzing multi-source mapping data, the identification of green connectivity obstacles in the urban ecological network and the inference of ecological corridor paths are realized.

[0028] Specifically, firstly, based on the vegetation cover characteristics and topographic elevation data of the candidate ecological source areas, green connectivity barriers are identified. By rasterizing and registering the vegetation index data and elevation data of the candidate ecological source areas, and using 20m×20m as the smallest spatial unit, the slope value, surface roughness and vegetation continuity index of each unit are calculated. When the slope of a unit exceeds 35 degrees or the continuity index is less than 0.4, the area is marked as a green connectivity barrier.

[0029] Furthermore, the identified green connectivity barriers are spatially overlaid with preset ecological connectivity evaluation data, which includes a surface permeability classification map, a land use type map, and an artificial disturbance index map. The connectivity barrier response data corresponding to each grid cell is calculated using a grid overlay algorithm, and the degree of obstruction of ecological connectivity in different areas is represented by a standardized scale of 0 to 1.

[0030] It needs to be explained that the ecological connectivity differentiation characteristics are calculated based on the response data of spatially formed connectivity barriers. The boundary transition relationship between high connectivity areas and low connectivity areas is analyzed in a spatial gradient manner to extract spatial channel areas with potential ecological connections. Finally, the spatial distribution of candidate ecological corridor paths is analyzed based on the ecological connectivity differentiation characteristics. Path associations are established between high connectivity areas through the minimum cumulative resistance algorithm, and multiple linear zonal structures with ecological connectivity potential are identified and marked as candidate ecological corridor paths.

[0031] Preferably, the green connectivity barriers are spatially overlaid with preset ecological connectivity evaluation data, and the connectivity barrier response data corresponding to each grid cell is calculated to infer the ecological connectivity differentiation characteristics. Identify the coordinates of multiple control feature points in the 3D scene of the green connectivity barrier and the preset ecological connectivity evaluation data; By controlling the coordinates of feature points, the coordinate system reference points of green connectivity barriers and ecological connectivity evaluation data are identified; The three-dimensional scene in the green connectivity obstacle and the ecological connectivity evaluation data are mapped to the same coordinate system, and geometric registration is performed based on the coordinate system reference point to generate an overlay three-dimensional scene. Using the coordinate system reference point as the fixed alignment grid cell point, calculate the connectivity obstacle response data of each grid cell in the superimposed 3D scene; Inferring grid cell resistance data from connectivity barrier response data; Ecological connectivity differentiation characteristics are mapped based on grid cell resistance data.

[0032] In this embodiment, the green connectivity barriers are spatially overlaid with preset ecological connectivity evaluation data, and the connectivity barrier response data corresponding to each grid cell is calculated to infer the ecological connectivity differentiation characteristics.

[0033] It needs to be explained that urban greening barrier information and ecological connectivity evaluation indicators are uniformly mapped and quantified in three-dimensional space to form gridded ecological connectivity resistance data.

[0034] Specifically, the coordinates of multiple control feature points in the 3D scene of the green connectivity obstacle and the preset ecological connectivity evaluation data are identified. These feature points may include road intersections, abrupt elevation changes, and important vegetation boundary nodes. By extracting the coordinates of the control feature points, spatial anchor points can be established for the 3D scene.

[0035] Furthermore, by controlling the coordinates of feature points, the coordinate system reference points of the green connectivity barrier and the ecological connectivity evaluation data are identified to determine a unified spatial benchmark for the two types of data, so as to ensure the accuracy of subsequent overlay analysis. Then, the three-dimensional scenes in the green connectivity barrier and the ecological connectivity evaluation data are mapped to the same coordinate system, and geometric registration is performed based on the coordinate system reference points. Through rotation, translation and scale adjustment, the spatial positions of the two types of scenes are made consistent, thereby generating an overlay three-dimensional scene.

[0036] Furthermore, using the coordinate system reference point as the fixed alignment grid cell point, the connectivity barrier response data of each grid cell in the superimposed 3D scene is calculated. This response data represents the blocking strength of the grid cell on ecological connectivity using a normalized scale of 0 to 1. The resistance data of each grid cell is inferred through the connectivity barrier response data. Based on the grid cell resistance data, the ecological connectivity differentiation characteristics are mapped out. The ecological accessibility and potential connectivity of different regions are reflected in the form of a spatial grid, thus obtaining the ecological connectivity differentiation characteristics.

[0037] Preferably, using the coordinate system reference point as the fixed alignment grid cell point, the specific steps for calculating the connectivity obstacle response data of each grid cell in the overlaid 3D scene are as follows: Using the coordinate system reference point as the fixed alignment grid cell point, the ecological resistance value of each grid cell position in the superimposed 3D scene is extracted, and the difference vector of ecological resistance values ​​between adjacent grid cells is calculated, thereby mapping the resistance gradient vector. Identify the boundaries of the two nearest candidate ecological source regions in the overlaid 3D scene, and calculate the shortest distance from each grid cell to the two candidate ecological source regions to obtain the dual-source distance parameters; The distance correction resistance coefficient of each grid cell is calculated based on the dual-source distance parameters and the resistance gradient vector, and the distance correction resistance coefficient is calibrated as the Euclidean distance connectivity loss value. By combining the resistance gradient vector with the Euclidean distance connectivity loss value, connectivity barrier response data is generated.

[0038] In this embodiment, the reference point of the coordinate system is used as the fixed alignment grid unit point to calculate the connectivity barrier response data of each grid unit in the superimposed three-dimensional scene, and the ecological resistance characteristics in the three-dimensional superimposed scene are quantified into gridded response data that can be used for ecological corridor path analysis.

[0039] Specifically, using the coordinate system reference point as the fixed alignment grid unit point, the ecological resistance value of each grid unit position in the superimposed 3D scene is extracted. This resistance value can be comprehensively evaluated based on slope, vegetation sparseness and human disturbance index, and the difference vector of ecological resistance values ​​between adjacent grid units is calculated, thereby mapping the resistance gradient vector.

[0040] Furthermore, the boundaries of the two nearest candidate ecological source areas in the overlaid 3D scene are identified, and the shortest distance from each grid cell to the two candidate ecological source areas is calculated using spatial indexing and shortest path search algorithms, thereby obtaining the dual-source distance parameters.

[0041] It needs to be explained that the distance-corrected resistance coefficient of each grid cell is calculated based on the dual-source distance parameters and the resistance gradient vector. This coefficient is used to reflect the spatial constraints of ecological passage. The distance-corrected resistance coefficient is calibrated as the Euclidean distance connectivity loss value. Furthermore, the resistance gradient vector and the Euclidean distance connectivity loss value are combined to form the final connectivity barrier response data. This data records the resistance magnitude and gradient direction with each grid cell as the smallest unit.

[0042] Preferably, the distance-corrected drag coefficient for each grid cell is calculated based on the dual-source distance parameters and the drag gradient vector as follows: Calculate the magnitude of the drag gradient vector for each grid cell to obtain the drag gradient magnitude; The dual-source distance parameter and the drag gradient magnitude are linearly weighted and combined, with the weighting coefficient of the dual-source distance parameter set to 0.6 and the weighting coefficient of the drag gradient magnitude set to 0.4. The linear weighted combination result is converted into a distance-corrected drag coefficient.

[0043] In this embodiment, the resistance characteristics of grid cells are combined with the spatial distribution of ecological source areas to quantify ecological access constraints.

[0044] Specifically, the drag gradient vector of each grid cell is obtained, and the drag gradient magnitude is obtained by calculating its magnitude, which represents the magnitude of the drag on ecological passage by the grid cell.

[0045] Furthermore, the shortest distance from each grid cell to the boundaries of the two nearest candidate ecological source areas is obtained as the dual-source distance parameter. Then, for each grid cell, the dual-source distance parameter is multiplied by a weight of 0.6, the resistance gradient magnitude is multiplied by a weight of 0.4, and the two are added together to obtain a linear weighted combination value.

[0046] Furthermore, the linear weighted combination value is normalized and converted into a distance correction drag coefficient between 0 and 1. Finally, the distance correction drag coefficient of each grid cell is recorded in the grid dataset.

[0047] Preferably, the spatial distribution of candidate ecological corridor paths is analyzed based on ecological connectivity differentiation characteristics as follows: Extract the locations of raster cells whose Euclidean distance connectivity loss value is lower than a preset connectivity loss threshold from the ecological connectivity differentiation features, and generate a set of highly connected raster cells; Spatial continuity detection is performed on highly connected grid cell sets to identify mutually continuous grid cell chains and mark them as candidate corridor paths; Calculate the deviation angle between the spatial extension direction of the candidate corridor path and the line connecting the candidate ecological source area. When the deviation angle is less than the preset deviation threshold, the candidate corridor path is marked as a candidate ecological corridor path.

[0048] In this embodiment, ecological connectivity differentiation features are transformed into spatial path data that can be used for corridor planning. The locations of grid cells with Euclidean distance connectivity loss values ​​lower than a preset threshold (e.g., a threshold of 0.3) are extracted from the ecological connectivity differentiation feature data to form a set of high connectivity grid cells.

[0049] Furthermore, spatial continuity detection is performed on this highly connected grid cell set. The grid cells can be traversed through a six-neighbor search or an eight-neighbor search to identify mutually continuous grid cell chains and mark the continuous grid cell chains as candidate corridor paths.

[0050] Specifically, the spatial extension direction of each candidate corridor path is calculated, and the angle deviation between the path and the corresponding candidate ecological source area is calculated. When the deviation angle is less than the preset deviation threshold (e.g., 30°), the candidate corridor path is determined as a candidate ecological corridor path. The entire process records the path affiliation information and extension direction of each grid cell.

[0051] Preferably, spatial continuity detection is performed on the set of highly connected grid cells to identify and mark consecutive grid cell chains as candidate corridor paths. Identify the starting grid cell in the set of highly connected grid cells and add the starting grid cell to the queue of grid cells to be expanded; Take the current grid cell from the queue of grid cells to be expanded, and search for unvisited neighboring grid cells that belong to the set of highly connected grid cells in the eight neighborhoods of the current grid cell. The searched neighboring raster cells are marked as visited and added to the queue of raster cells to be expanded, and at the same time, they are added to the current raster cell chain; Iteratively search and mark all grid cells in the queue of grid cells to be expanded until the queue of grid cells to be expanded is empty, thereby identifying all consecutive grid cell chains and marking each consecutive grid cell chain as a candidate corridor path.

[0052] In this embodiment, for spatial continuity detection of highly connected grid cell sets, discrete highly connected grid cells are transformed into continuous spatial paths.

[0053] Specifically, for spatial continuity detection of highly connected raster cell sets, discrete highly connected raster cells are transformed into continuous spatial paths to facilitate subsequent identification of candidate ecological corridor paths.

[0054] Specifically, the starting grid cell is first identified from the set of highly connected grid cells. This starting grid cell can be obtained by calculating the grid cell closest to the candidate ecological source area.

[0055] Furthermore, the starting grid cell is added to the queue of grid cells to be expanded, and the current grid cell chain is initialized. The spatial coordinates and sequence number of the grid cell are recorded to track the continuity and direction information of the path.

[0056] Furthermore, the current grid cell is retrieved from the queue of grid cells to be expanded, and its eight neighborhoods (up, down, left, right and four diagonal directions) are traversed. Grid cells that belong to the set of highly connected grid cells in the neighborhood and have not yet been visited are searched. These grid cells are marked as visited to prevent duplicate searches. At the same time, they are added to the queue of grid cells to be expanded and included in the current grid cell chain to maintain the continuity of the path and the spatial structure.

[0057] Furthermore, for each neighboring raster cell added to the raster cell chain, the above iterative search operation is repeated until the queue of raster cells to be expanded is empty, thus completing the full expansion of the current raster cell chain.

[0058] Furthermore, after completing a grid cell chain, the next starting grid cell is selected from the remaining unvisited set of highly connected grid cells. The above steps are repeated iteratively until all grid cells have been visited, thereby identifying all consecutive grid cell chains in the set. The spatial coordinates, length, orientation information, and neighborhood relationships of each grid cell chain are recorded. Finally, each consecutive grid cell chain is marked as a candidate corridor path.

[0059] Preferably, the deviation angle between the spatial extension direction of the candidate corridor path and the direction connecting the candidate ecological source areas is calculated as follows: Extract the center point coordinates of the grid cells at both ends of the candidate corridor path and construct the main direction vector of the candidate corridor path; Identify the two nearest ecological source candidate regions that are spatially associated with the candidate corridor path, and extract the centroid coordinates of the two ecological source candidate regions; Construct a direction vector for connecting the two candidate ecological source areas based on their centroid coordinates. Calculate the angle between the main direction vector of the candidate corridor path and the direction vector of the line connecting the ecological source areas, and label this angle as the deviation angle.

[0060] In this embodiment, the purpose of calculating the deviation angle between the spatial extension direction of the candidate corridor path and the direction of the line connecting the candidate ecological source area is to determine the directional consistency between the candidate corridor path and the spatial layout of the source area.

[0061] Specifically, the center point coordinates of the first and last grid cells are first extracted from the candidate corridor path. The center point coordinates of each grid cell are represented by its geographic coordinates or grid row and column index. Based on the center point coordinates of the first and last cells, the main direction vector of the candidate corridor path is constructed to represent the spatial extension direction of the path.

[0062] Furthermore, the two nearest candidate ecological source areas spatially associated with the candidate corridor path are identified. The spatial centroid of each candidate ecological source area is calculated by averaging the coordinates of the center of all grid cells within the area to obtain the centroid coordinates, so as to determine the direction of the spatial connection between the source areas.

[0063] It should be explained that a direction vector for connecting the two ecological source areas is constructed based on the centroid coordinates of the two candidate ecological source areas. This vector is used to represent the direction of the most direct spatial connection between the two source areas.

[0064] Furthermore, the angle between the main direction vector of the candidate corridor path and the direction vector connecting the ecological source area is calculated. The angle can be obtained through the relationship between the vector dot product and the modulus. The obtained angle value is the deviation angle.

[0065] Furthermore, the calculated deviation angle is compared with the preset deviation threshold. When the deviation angle is less than the threshold, the candidate corridor path is marked as a candidate ecological corridor path with the same direction.

[0066] Preferably, the direction vector connecting the two candidate ecological source areas is constructed based on the centroid coordinates of the two ecological source areas as follows: Calculate the mean x-coordinate and mean y-coordinate of all grid cells within the first ecological source candidate area to obtain the geocentric coordinates of the first ecological source. Calculate the mean x-coordinate and mean y-coordinate of all grid cells within the candidate area of ​​the second ecological source to obtain the geocentric coordinates of the second ecological source. Calculate the positional difference between the coordinates of the second ecological source geological center and the coordinates of the first ecological source geological center, and construct a direction vector from the first ecological source geological center to the second ecological source geological center based on the positional difference; The direction vector is normalized to generate the direction vector connecting the ecological source areas.

[0067] In this embodiment, please refer to Figure 3 The purpose of constructing the direction vector connecting the two ecological source areas based on the centroid coordinates of the two candidate ecological source areas is to clarify the spatial pointing relationship between the two source areas.

[0068] Specifically, the mean values ​​of the x-coordinate and y-coordinate of all grid cells within the first candidate ecological source area (denoted as C1) are calculated to obtain the centroid coordinates of the first ecological source area, which can be represented by two-dimensional or three-dimensional coordinates.

[0069] Furthermore, the mean values ​​of the x and y coordinates of all grid cells within the second ecological source candidate area (denoted as C2) are calculated to obtain the centroid coordinates of the second ecological source area.

[0070] Furthermore, the coordinate difference between the second ecological source geological center coordinates and the first ecological source geological center coordinates is calculated. The coordinate difference represents the spatial displacement between the two centroids in the horizontal and vertical directions, respectively.

[0071] It should be explained that a direction vector is constructed based on this positional difference, pointing from the first ecological source geological center to the second ecological source geological center. The starting point of this vector is the first ecological source geological center, and the ending point is the second ecological source geological center.

[0072] Furthermore, the direction vector is normalized by standardizing its length to 1, thereby generating a standardized direction vector for connecting ecological source areas.

[0073] Of particular importance is that step S4 is as follows: The spatial coordinate distribution of source area grids in the candidate region of ecological source area is calculated based on the intersection points of spatial coverage accuracy. Ecological density gradients are obtained based on spatial coordinate distribution and spatiotemporal registration relationships; ecological node regions in candidate ecological source areas are identified through ecological density gradients. Calculate the rate of ecological change and gradient direction stability in the ecological node region, and identify the core node region based on the rate of ecological change and gradient direction stability; By combining the potential paths of ecological corridors and the core node areas, the connection paths are calculated and optimized to obtain the urban ecological corridor network structure. Urban ecological corridor planning schemes are generated based on the urban ecological corridor network structure.

[0074] In this embodiment, the spatial coordinate distribution of source area grids in the candidate ecological source area is calculated based on the intersection points of spatial coverage accuracy. By spatially overlaying the multi-source mapping data obtained in the early stage, high-resolution remote sensing images, digital elevation models and land cover data are registered in a unified coordinate system. The candidate ecological source area is divided into regular grids with a grid size of 30 meters. The spatial coverage accuracy intersection points are extracted for each grid unit, that is, the intersection positions of spatial feature boundaries between different data layers. The geographic coordinates and accuracy attributes of these intersection points are recorded as the spatial coordinate distribution data of the source area grids.

[0075] It should be explained that when calculating the crossover point, an error-weighted average is performed by comparing the resolution weight values ​​of each data layer. Specifically, the ecological density gradient is obtained based on the spatial coordinate distribution and spatiotemporal registration relationship. By integrating the ecological index data of the same area at different time periods through time series integration, the surface vegetation index NDVI, surface humidity index NDWI, and land cover distribution intensity values ​​are standardized to normalized values ​​in the range of 0 to 1. Under the same spatial reference system, the variation amplitude of each grid in the X and Y directions is calculated to form an ecological density gradient dataset.

[0076] Furthermore, ecological node regions in the candidate regions of ecological source areas are identified by ecological density gradient. In this process, a density gradient threshold is set. When the gradient value is higher than 1.2 times the regional average and is spatially continuous for more than 4 grids, it is defined as an ecological node region.

[0077] It should be explained that when calculating the rate of ecological change and the stability of gradient direction in the ecological node region, the change trend of the same node in three consecutive time periods is calculated in the time series ecological index. The positive or negative value of the change magnitude is used to determine the continuous direction of the ecological state. Then, the difference in gradient vector angle is statistically analyzed. If the gradient direction change between adjacent time periods is less than 10 degrees, it is determined to be a stable direction.

[0078] Furthermore, core node regions are identified based on ecological change rate and gradient direction stability. Clustering algorithms are used to classify and filter these regions, retaining nodes with change rates greater than the average and stability above 90% as core nodes. Specifically, after obtaining the core node area, the connection path is calculated and optimized by combining the potential ecological corridor path and the core node area. First, a regional ecological resistance grid model is established with three major constraint parameters: topographic slope, land use resistance, and vegetation coverage. Then, with the spatial coordinates of the core node as the starting point and the ending point, the minimum cumulative resistance algorithm is used to search for a set of feasible paths, calculate the connectivity coefficient and resistance cumulative value of each path, and select the optimal path set based on the comprehensive evaluation index of minimum resistance and maximum connectivity, thereby generating the urban ecological corridor network structure. Furthermore, based on the urban ecological corridor network structure, an urban ecological corridor planning scheme is generated. By overlaying the core node vector layer, the optimized path layer, and the land type background layer on the same geographic information system, spatial analysis tools are used to generate the hierarchical distribution of the main and secondary corridor lines. The accessibility index and coverage area ratio of each corridor segment are calculated to form an urban ecological corridor planning layer that includes spatial geometry, ecological level, and network hierarchy.

[0079] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0080] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for intelligent construction of urban ecological corridors based on multi-source surveying and mapping data, characterized in that, Includes the following steps: Step S1: Acquire satellite remote sensing images and topographic elevation data and integrate them into multi-source mapping data; identify the spatiotemporal reference characteristics of each data source in the multi-source mapping data; extract land cover type characteristics and vegetation cover characteristics from the multi-source mapping data; Step S2: Construct the spatiotemporal registration relationship of multi-source mapping data based on spatiotemporal reference characteristics; The spatiotemporal registration relationship is used to link land cover type characteristics with vegetation cover characteristics, and candidate areas of ecological source area are screened. Step S3: Identify green connectivity barriers based on candidate ecological source areas; identify candidate ecological corridor paths using preset ecological connectivity evaluation data and green connectivity barriers; Step S4: Reconstruct the ecological node areas with spatial relationships in the candidate ecological source areas, and generate the urban ecological corridor network structure based on the ecological node areas and candidate ecological corridor paths.

2. The method for intelligent construction of urban ecological corridors based on multi-source mapping data as described in claim 1, characterized in that, In step S2, the spatiotemporal registration relationship of multi-source mapping data is constructed based on spatiotemporal reference characteristics as follows: Reconstruct the spatial coverage accuracy of each data source in the multi-source mapping data based on spatiotemporal reference characteristics; The data fusion weighting coefficients are calculated by spatial coverage accuracy, and the spatiotemporal reference characteristics are corrected by the data fusion weighting coefficients, thereby generating standardized mapping parameters. Establish the spatiotemporal correspondence between spatial coverage accuracy and standardized surveying parameters, and construct the spatiotemporal registration relationship of multi-source surveying data based on the spatiotemporal correspondence between spatial coverage accuracy and standardized surveying parameters.

3. The method for intelligent construction of urban ecological corridors based on multi-source mapping data as described in claim 1, characterized in that, Step S3 is as follows: Identify green connectivity barriers based on vegetation cover characteristics and topographic elevation data of candidate ecological source areas; Spatial overlay analysis is performed on green connectivity barriers and preset ecological connectivity evaluation data, and the connectivity barrier response data corresponding to each grid cell is calculated to infer the ecological connectivity differentiation characteristics. The spatial distribution of candidate ecological corridor paths is analyzed based on the differentiation characteristics of ecological connectivity in order to identify candidate ecological corridor paths.

4. The method for intelligent construction of urban ecological corridors based on multi-source mapping data as described in claim 3, characterized in that, Spatially overlaying green connectivity barriers with pre-defined ecological connectivity evaluation data and calculating the connectivity barrier response data for each grid cell are performed to infer the specific ecological connectivity differentiation characteristics: Identify the coordinates of multiple control feature points in the 3D scene of the green connectivity barrier and the preset ecological connectivity evaluation data; By controlling the coordinates of feature points, the coordinate system reference points of green connectivity barriers and ecological connectivity evaluation data are identified; The three-dimensional scene in the green connectivity obstacle and the ecological connectivity evaluation data are mapped to the same coordinate system, and geometric registration is performed based on the coordinate system reference point to generate an overlay three-dimensional scene. Using the coordinate system reference point as the fixed alignment grid cell point, calculate the connectivity obstacle response data of each grid cell in the superimposed 3D scene; Inferring grid cell resistance data from connectivity barrier response data; Ecological connectivity differentiation characteristics are mapped based on grid cell resistance data.

5. The method for intelligent construction of urban ecological corridors based on multi-source mapping data as described in claim 4, characterized in that, Using the coordinate system reference point as the fixed alignment grid cell point, the connection obstacle response data of each grid cell in the overlaid 3D scene is calculated as follows: Using the coordinate system reference point as the fixed alignment grid cell point, the ecological resistance value of each grid cell position in the superimposed 3D scene is extracted, and the difference vector of ecological resistance values ​​between adjacent grid cells is calculated, thereby mapping the resistance gradient vector. Identify the boundaries of the two nearest candidate ecological source regions in the overlaid 3D scene, and calculate the shortest distance from each grid cell to the two candidate ecological source regions to obtain the dual-source distance parameters; The distance correction resistance coefficient of each grid cell is calculated based on the dual-source distance parameters and the resistance gradient vector, and the distance correction resistance coefficient is calibrated as the Euclidean distance connectivity loss value. By combining the resistance gradient vector with the Euclidean distance connectivity loss value, connectivity barrier response data is generated.

6. The method for intelligent construction of urban ecological corridors based on multi-source mapping data as described in claim 5, characterized in that, The distance-corrected drag coefficient for each grid cell is calculated based on the dual-source distance parameters and drag gradient vector as follows: Calculate the magnitude of the drag gradient vector for each grid cell to obtain the drag gradient magnitude; The dual-source distance parameter and the drag gradient magnitude are linearly weighted and combined, with the weighting coefficient of the dual-source distance parameter set to 0.6 and the weighting coefficient of the drag gradient magnitude set to 0.

4. The linear weighted combination result is converted into a distance-corrected drag coefficient.

7. The method for intelligent construction of urban ecological corridors based on multi-source mapping data as described in claim 3, characterized in that, Based on the analysis of ecological connectivity differentiation characteristics, the spatial distribution of candidate ecological corridor paths is as follows: Extract the locations of raster cells whose Euclidean distance connectivity loss value is lower than a preset connectivity loss threshold from the ecological connectivity differentiation features, and generate a set of highly connected raster cells; Spatial continuity detection is performed on highly connected grid cell sets to identify mutually continuous grid cell chains and mark them as candidate corridor paths; Calculate the deviation angle between the spatial extension direction of the candidate corridor path and the line connecting the candidate ecological source area. When the deviation angle is less than the preset deviation threshold, the candidate corridor path is marked as a candidate ecological corridor path.

8. The method for intelligent construction of urban ecological corridors based on multi-source mapping data as described in claim 7, characterized in that, Spatial continuity detection is performed on highly connected grid cell sets to identify and mark consecutive grid cell chains as candidate corridor paths. Identify the starting grid cell in the set of highly connected grid cells and add the starting grid cell to the queue of grid cells to be expanded; Take the current grid cell from the queue of grid cells to be expanded, and search for unvisited neighboring grid cells that belong to the set of highly connected grid cells in the eight neighborhoods of the current grid cell. The searched neighboring raster cells are marked as visited and added to the queue of raster cells to be expanded, and at the same time, they are added to the current raster cell chain; Iteratively search and mark all grid cells in the queue of grid cells to be expanded until the queue of grid cells to be expanded is empty, thereby identifying all consecutive grid cell chains and marking each consecutive grid cell chain as a candidate corridor path.

9. The method for intelligent construction of urban ecological corridors based on multi-source mapping data as described in claim 7, characterized in that, The specific angle of deviation between the spatial extension direction of the candidate corridor path and the direction connecting the candidate ecological source areas is calculated as follows: Extract the center point coordinates of the grid cells at both ends of the candidate corridor path and construct the main direction vector of the candidate corridor path; Identify the two nearest ecological source candidate regions that are spatially associated with the candidate corridor path, and extract the centroid coordinates of the two ecological source candidate regions; Construct a direction vector for connecting the two candidate ecological source areas based on their centroid coordinates. Calculate the angle between the main direction vector of the candidate corridor path and the direction vector of the line connecting the ecological source areas, and label this angle as the deviation angle.

10. The method for intelligent construction of urban ecological corridors based on multi-source mapping data as described in claim 7, characterized in that, The direction vector connecting the two candidate ecological source areas is constructed based on their centroid coordinates as follows: Calculate the mean x-coordinate and mean y-coordinate of all grid cells within the first ecological source candidate area to obtain the geocentric coordinates of the first ecological source. Calculate the mean x-coordinate and mean y-coordinate of all grid cells within the candidate area of ​​the second ecological source to obtain the geocentric coordinates of the second ecological source. Calculate the positional difference between the coordinates of the second ecological source geological center and the coordinates of the first ecological source geological center, and construct a direction vector from the first ecological source geological center to the second ecological source geological center based on the positional difference; The direction vector is normalized to generate the direction vector connecting the ecological source areas.

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