A method, system and medium for identifying and optimizing an ecological network of territorial space

Abstract

This invention relates to the field of land planning and ecological information processing technology, and particularly to a method, system, and medium for identifying and optimizing a national spatial ecological network. The method includes the following steps: collecting ecological environment monitoring data and acquiring national spatial characteristic data; comparing the ecological environment monitoring data with preset ecological environment benchmark data to identify characteristics of ecological environment status changes and initiate ecological network structure updates; extracting topographic elevation information and vegetation vertical structure information from the national spatial characteristic data to establish a three-dimensional connectivity judgment standard for species mobility adaptation. This invention, by constructing a three-dimensional connectivity judgment standard and implementing differentiated corridor integration optimization, achieves synchronous adaptation of the ecological network and ecological conditions, and ensures that corridors meet the three-dimensional mobility needs of species, thereby enhancing the dynamic adaptability, species specificity, and connectivity stability of the ecological network.

Description

Technical Field

[0001] This invention relates to the field of land planning and ecological information processing technology, and in particular to a method, system and medium for identifying and optimizing the ecological network of national land space. Background Technology

[0002] The national spatial ecological network is a core technological system that achieves ecosystem connectivity and stability by identifying ecological source areas and constructing ecological corridors. Its core objective is to safeguard biodiversity, and the key lies in accurately capturing changes in ecological conditions and scientifically determining corridor connectivity. Existing technologies are mostly based on data such as land use type and vegetation cover, identifying ecological source areas and corridors through planar analysis and optimizing network structure using periodic data update patterns. However, this technological approach has significant limitations: in terms of ecological condition response, it relies on batch processing of static data with a unified benchmark, without establishing differentiated monitoring mechanisms for different ecosystem types, making it difficult to dynamically track habitat changes and achieve automatic adaptive adjustments to the network structure; in terms of corridor connectivity determination, it only focuses on two-dimensional planar features, without considering three-dimensional spatial factors such as changes in topographic elevation and vertical vegetation stratification, ignoring the specific adaptation needs of species to their living spaces, resulting in the constructed network failing to play a practical ecological connectivity role. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, system, and medium for identifying and optimizing the ecological network of territorial space in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for identifying and optimizing territorial spatial ecological networks is provided, the method comprising the following steps:

[0005] Step S1: Collect ecological environment monitoring data and obtain land space characteristic data;

[0006] Step S2: Based on the comparison between the ecological environment monitoring data and the preset ecological environment benchmark data, identify the characteristics of ecological environment status changes and initiate the ecological network structure update.

[0007] Step S3: Extract topographic elevation information and vegetation vertical structure information from the land space feature data to establish a three-dimensional connectivity judgment standard for species mobility adaptation.

[0008] Step S4: In response to the update of the ecological network structure, the initial corridors of the ecological source area are identified and determined through the two-dimensional spatial data in the land spatial feature data. The initial corridors are verified based on the three-dimensional connectivity judgment criteria, and the adapted passage corridors are retained and corridor optimization direction data is generated.

[0009] Step S5: Combining the characteristics of ecological environment changes, integrate the data on suitable passageways and optimized corridor directions to output a three-dimensional effective corridor network of ecological source areas in the national territory.

[0010] Preferably, the present invention also provides a land space ecological network identification and optimization system for performing the above-described land space ecological network identification and optimization method, the land space ecological network identification and optimization system comprising:

[0011] The data acquisition module is used to collect ecological and environmental monitoring data and obtain land space characteristic data;

[0012] The ecological anomaly identification module is used to compare ecological environment monitoring data with preset ecological environment benchmark data, identify the characteristics of ecological environment status anomalies, and initiate ecological network structure updates.

[0013] The three-dimensional connectivity establishment module is used to extract topographic elevation information and vegetation vertical structure information from land spatial feature data, and establish three-dimensional connectivity judgment criteria for species mobility adaptation.

[0014] The initial corridor optimization module is used to respond to the update of the ecological network structure. It identifies and determines the initial corridors of the ecological source area through two-dimensional spatial data in the land spatial feature data, verifies the initial corridors based on the three-dimensional connectivity judgment standard, retains the suitable passage corridors and generates corridor optimization direction data.

[0015] The 3D effective corridor network output module is used to combine the characteristics of ecological environment status changes, integrate and match the data of passage corridors and corridor optimization, and output the 3D effective corridor network of the ecological source areas of the national land space.

[0016] Preferably, the present invention also provides a computer storage medium storing a computer program, which, when executed, implements the above-described method for identifying and optimizing the national spatial ecological network.

[0017] The beneficial effects of this invention are:

[0018] First, based on the type of ecosystem, separate zones are divided and dedicated benchmark data is configured. By comparing time series data, deviation values ​​are calculated for each time period. Combined with the slope of the change curve and the threshold of the stable interval, the abnormal area, the spread range and the duration are accurately identified. The response mechanism that triggers network updates forms a dynamic linkage with the changes in the ecological environment, ensuring that the network structure and the ecological condition are synchronously adapted.

[0019] Second, extract information on continuous changes in topographic elevation and vertical stratification of vegetation, divide gradient intervals and permeability intervals, and establish a two-dimensional cross-correlation standard. Define the adaptation range by accumulating the slope of continuous spatial segments and statistically analyzing the proportion of gaps in vegetation layers. This enables the dynamic binding of three-dimensional connectivity judgment standards with spatial location, ensuring that the corridor adaptability strictly meets the three-dimensional spatial requirements for species passage.

[0020] Third, the region is divided into four categories based on the proportion of anomalous coverage and a differentiated corridor integration strategy is implemented. Through corridor segment verification and optimal alternative path screening (with spatial curvature and length as the core screening indicators), combined with edge coordinate matching, topological relationship calibration and isolated node supplementation, a three-dimensional corridor network with continuous coordinates, no breaks and no overlap is formed. Each corridor is associated with the regional dominant species adaptation parameters, taking into account both network connectivity stability and species-specific adaptation characteristics. Attached Figure Description

[0021] Figure 1 A schematic diagram illustrating the steps of a method for identifying and optimizing a territorial spatial ecological network;

[0022] Figure 2 This is a flowchart illustrating step S2 in this embodiment;

[0023] Figure 3 This is a schematic diagram of the territorial spatial ecological network in this embodiment;

[0024] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0025] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0028] To achieve the above objectives, please refer to Figures 1 to 3 A method for identifying and optimizing the ecological network of territorial space, the method comprising the following steps:

[0029] Preferably, step S1: collect ecological environment monitoring data and obtain land space characteristic data;

[0030] In this embodiment, ecological environment monitoring data is collected through ground monitoring stations, satellite remote sensing platforms, UAV aerial survey systems, and ecological environment monitoring sensor networks. The ground monitoring stations are deployed in a 1km×1km grid to simultaneously collect indicators such as atmospheric particulate matter (PM2.5, PM10) concentration, soil moisture content, soil heavy metal (lead, cadmium, mercury) content, surface runoff velocity, groundwater level, and water quality (pH value, chemical oxygen demand, ammonia nitrogen content). The sampling frequency is once per hour, and the data accuracy is retained to three decimal places. The satellite remote sensing platform uses a combination of Gaofen-6 satellite and Sentinel-2 satellite to acquire data such as Normalized Difference Vegetation Index (NDVI), vegetation cover, land use type, and water area ratio with a spatial resolution of 10m. The image acquisition time is selected during clear and cloudless periods, and the data format is GeoTIFF.

[0031] In one embodiment, the UAV aerial survey system is equipped with a multispectral camera and a thermal infrared sensor to conduct low-altitude aerial surveys of key ecological areas. The flight altitude is set at 150m, and the aerial survey overlap is ≥60% laterally and ≥30% longitudinally. Data such as vegetation canopy temperature, leaf chlorophyll content, and surface micro-topography undulations are collected, and the output format is ENVI standard image files. An ecological environment monitoring sensor network is deployed at 500m intervals to continuously collect real-time data such as relative humidity, ambient temperature, soil organic matter content, and vegetation transpiration rate. Data transmission adopts LoRa wireless communication technology, and the transmission delay is controlled within 30s.

[0032] In another embodiment, land space feature data are obtained through the National Geographic Information Public Service Platform, the Natural Resources "One Map" Database, and the Ecological Protection Red Line Delineation Results Database. The three-dimensional spatial basic data includes 1:50000 digital elevation model (DEM) data (elevation accuracy ±2m, grid size 30m×30m) and vegetation vertical stratification data (including height range, diameter at breast height, crown coverage, and other indicators of tree layer, shrub layer, and herb layer). Data collection adopts the quadrat survey method, with a quadrat area of ​​20m×20m, and each quadrat is surveyed at least 3 times.

[0033] In another embodiment, the two-dimensional spatial basic data includes a land use status map (scale 1:10000, land categories classified to the second level), an ecological function zoning map, and boundary vector data of special control areas (core areas of nature reserves, basic farmland, permanent basic farmland, drinking water source protection areas, etc.) (the coordinate system adopts the CGCS2000 national geodetic coordinate system, and the vector data format is Shapefile). All acquired data are stored in a PostgreSQL spatial database after unified coordinate transformation and format standardization.

[0034] Preferably, step S2: Based on the comparison between ecological environment monitoring data and preset ecological environment benchmark data, identify the characteristics of ecological environment status changes, and initiate ecological network structure updates;

[0035] Optionally, step S2 includes the following steps:

[0036] Step S21: Identify the ecosystem types in the ecological environment monitoring data;

[0037] Step S22: Divide the area into independent zones according to ecosystem type, and configure exclusive ecological and environmental baseline data for each independent zone;

[0038] Step S23: Split the ecological environment monitoring data into independent zones, and use the time series rolling comparison method to match the exclusive ecological environment benchmark data of the corresponding independent zone at fixed time intervals, and calculate the comparison deviation value for each time period;

[0039] Step S24: Track the change curve of the comparison deviation value. When the slope of the change curve is continuously positive and the comparison deviation value breaks through the preset stable range, locate the ecological environment change area, simultaneously mark the range and duration of the change, and start the ecological network structure update.

[0040] In this embodiment, based on the Normalized Difference Vegetation Index (NDVI), land use type, vegetation coverage, water area ratio, and surface runoff velocity in the ecological environment monitoring data, four types of ecosystems are classified: vegetation, aquatic, artificial, and semi-natural, according to the judgment rules of NDVI≥0.6, water area ratio≥70%, construction land ratio≥50%, soil organic matter content≥3%, and vegetation coverage 20%-60%. Each independent zone is identified by a 10-digit code (the first 4 digits are the type code, and the last 6 digits are the location code).

[0041] In one embodiment, historical monitoring data from each zone for more than 10 years are retrieved and arithmetic averaged to obtain specific baseline data. The baseline for vegetation ecosystems is annual average NDVI 0.72±0.05, soil moisture content 25%±3%, and vegetation transpiration rate 3.2mm / d±0.4mm / d. The baseline for aquatic ecosystems is pH 7.2±0.3, chemical oxygen demand 20mg / L±5mg / L, and ammonia nitrogen content 0.5mg / L±0.1mg / L. The baseline for artificial ecosystems is PM2.5 concentration 35μg / m³±5μg / m³ and ambient temperature 22℃±2℃. The baseline for semi-natural ecosystems is soil organic matter content 4.5%±0.5% and surface micro-topographic relief 5m±1m. The monitoring data is split according to the zone code, and the time-period comparison deviation is calculated at fixed intervals of 24 hours using the formula (current monitoring value - baseline value) / baseline value × 100% (retaining two decimal places).

[0042] In another embodiment, linear regression is used to track the deviation value change curve. The slope is set to be positive for 7 consecutive time periods and the deviation value exceeds the corresponding stable range (NDVI [-10%, 10%], pH value [-5%, 5%], PM2.5 concentration [-15%, 15%]) as the judgment condition. The abnormal area is located by coordinate matching. The diffusion range is marked by expanding the buffer radius by 500m for every 10% increase in deviation. The start and duration time periods (accurate to the hour) are recorded to trigger the ecological network structure update program. The transmission delay is controlled within 10 seconds.

[0043] Preferably, step S3: extract topographic elevation information and vegetation vertical structure information from the land space feature data, and establish a three-dimensional connectivity determination standard for species mobility adaptation;

[0044] Optionally, step S3 involves extracting topographic elevation information and vegetation vertical structure information from the land spatial feature data as follows:

[0045] Three-dimensional spatial basic data are separated from land spatial feature data to extract information on continuous changes in topographic elevation and vertical stratification of vegetation.

[0046] Calculate the gradient point by point for the continuously changing terrain elevation information, and divide the gradient interval;

[0047] The vertical stratification structure of vegetation is analyzed to detect interlayer gaps and delineate permeability intervals.

[0048] Using gradient intervals and transparency intervals as dimensions, a three-dimensional connectivity judgment standard with two-dimensional cross-correlation is established.

[0049] In this embodiment, 1:50000 digital elevation model (DEM) data (elevation accuracy ±2m, grid size 30m×30m) and vegetation vertical stratification data (including height range, diameter at breast height, and crown coverage of tree, shrub, and herb layers, quadrat area 20m×20m, each quadrat repeated 3 times) are separated from the land spatial feature data. The continuous change information of topographic elevation and the vertical stratification structure information of vegetation are extracted. The gradient calculation formula is used to calculate the slope value of the continuous change information of topographic elevation for each grid node. The slope is divided into 5 gradient intervals according to the slope values ​​0°-5°, 5°-15°, 15°-30°, 30°-45°, and >45°, and each interval is assigned a unique gradient code.

[0050] In another embodiment, the vertical distance between each layer in the vegetation vertical stratification structure information is measured on a sample basis. The ratio of the width of the interlayer gap to the height of the layer is statistically analyzed. The layer is divided into 5 transparency intervals according to the ratios of 0%-20%, 20%-40%, 40%-60%, 60%-80%, and 80%-100%, and each interval is assigned a unique transparency code. A 5×5 two-dimensional cross-correlation matrix is ​​constructed with the gradient interval as the horizontal dimension and the transparency interval as the vertical dimension. Each cross cell in the matrix corresponds to a set of three-dimensional connectivity judgment rules, which clarifies the allowable range of elevation adaptation and the allowable range of vertical spatial adaptation under the combination, forming a three-dimensional connectivity judgment standard. The standard data is precisely correlated with the national land spatial coordinate system (CGCS2000 National Geodetic Coordinate System).

[0051] Optionally, step S3, when establishing the three-dimensional connectivity criterion, includes:

[0052] For gradient intervals, gradient levels are divided by accumulating the slope of continuous spatial segments, and the allowable range of elevation adaptation is defined according to the gradient level.

[0053] For the permeability range, the permeability level is divided by statistically analyzing the proportion of gaps between each vegetation layer, and the allowable range of vertical space adaptation is defined according to the permeability level.

[0054] The allowable range of elevation adaptation is spatially superimposed and coupled with the allowable range of vertical space adaptation so that the standard boundary is dynamically associated with the spatial location.

[0055] In this embodiment, please refer to Figure 2For the gradient intervals of 0°-5°, 5°-15°, 15°-30°, 30°-45°, and >45°, continuous spatial segments are divided in 100m units. The slope values ​​of all grid nodes in each spatial segment are accumulated and calculated. The accumulated results correspond to the gradient levels I-V (accumulated values ​​of 0°-500° are level I, 500°-1500° are level II, 1500°-3000° are level III, 3000°-4500° are level IV, and >4500° are level V). The allowable range of elevation adaptation is defined according to the level, where level I allows elevation changes of ±5m, level II allows ±10m, level III allows ±20m, level IV allows ±30m, and level V allows ±40m.

[0056] In another embodiment, please refer to Figure 3 For the permeability ranges of 0%-20%, 20%-40%, 40%-60%, 60%-80%, and 80%-100%, the proportion of intervegetative gaps in each vegetation layer of all sample plots within each range was statistically analyzed. After taking the average, permeability levels were classified into A, E, and E grades (average 0%-20% is grade A, 20%-40% is grade B, 40%-60% is grade C, 60%-80% is grade D, and 80%-100% is grade E). The allowable range for vertical spatial adaptation was defined according to the grade, with grade A allowing vertical passage space. The elevation range is ≥3m for Class B, ≥2.5m for Class C, ≥2m for Class D, ≥1.5m for Class E, and ≥1m for Class E. Spatial coordinate overlay technology is used to couple the allowable range of elevation adaptation corresponding to each gradient level with the allowable range of vertical spatial adaptation corresponding to the transparency level at the same spatial location. This ensures that each land space coordinate point corresponds to a unique set of elevation and vertical spatial adaptation combination rules, realizing the dynamic association of standard boundaries with spatial location. The association accuracy is consistent with the 30m×30m grid size of the land space feature data.

[0057] Of particular importance is that step S3 involves spatially superimposing and coupling the allowable range of elevation adaptation with the allowable range of vertical spatial adaptation, so that the standard boundary is dynamically associated with spatial location. Specifically:

[0058] Extracting the spatial activity range of dominant species in different regions from land spatial feature data;

[0059] Match the spatial activity range of dominant species with the spatial range of the three-dimensional connectivity determination criteria;

[0060] Based on the elevation change characteristics and vegetation structure characteristics within the activity area, calibrate the allowable range parameters of elevation adaptation and vertical space adaptation for the corresponding area.

[0061] The three-dimensional connectivity criteria are adjusted according to the differentiated adaptation needs of regional dominant species, so that the criteria correspond to the regional spatial coordinates.

[0062] In this embodiment, spatial activity range data of dominant species in forest ecosystems (such as macaques and leopard cats), wetland ecosystems (such as egrets and otters), and grassland ecosystems (such as gazelles and foxes) are extracted from land spatial feature data. The data includes the boundary coordinates of the core activity areas of species foraging, breeding, and migration. The core activity areas are delineated according to species footprint surveys and infrared camera monitoring data, with a monitoring period of 12 months. At least 500 valid activity point data are collected for each species. Spatial coordinate matching technology is used to accurately align the boundary coordinates of the core activity areas of each dominant species with the spatial range of the three-dimensional connectivity determination criteria, ensuring that the spatial projection error between the species activity range and the determination criteria does not exceed 30m.

[0063] In another embodiment, by extracting the topographic elevation variation characteristics (maximum elevation difference, average slope) and vegetation structure characteristics (dominant vegetation layer height, average interlayer gap) within the activity range of each species, the allowable elevation and vertical spatial adaptation range parameters of the corresponding areas are calibrated according to the species activity characteristics. Specifically, for the macaque activity area, the original allowable elevation range of ±20m for the Level III gradient is calibrated to ±15m, and the allowable vertical spatial adaptation range of ≥2m is calibrated to ≥2.2m. For the otter activity area, the original allowable elevation range of ±10m for the Level II gradient is calibrated to ±8m. The allowable range for vertical spatial adaptation is calibrated from ≥2.5m to ≥1.8m. In the activity area of ​​the Tibetan gazelle, the original allowable range of Level I gradient elevation ±5m is calibrated to ±7m, and the allowable range for vertical spatial adaptation ≥3m is calibrated to ≥2.8m. Based on the differentiated adaptation parameters of the dominant species in the region, the matrix threshold of the three-dimensional connectivity judgment standard is adjusted. A one-to-one correspondence is established between each 30m×30m grid node in the national territory and the adjusted judgment standard, so that the standard boundary is dynamically associated with the spatial coordinates of the grid node. The associated data is stored in the spatial database and bound to the species type code.

[0064] Preferably, step S4: In response to the update of the ecological network structure, the initial corridors of the ecological source area are identified and determined through the two-dimensional spatial data in the land spatial feature data, the initial corridors are verified based on the three-dimensional connectivity judgment criteria, the adapted passage corridors are retained and the corridor optimization direction data is generated.

[0065] Optionally, step S4, which involves identifying and determining the initial corridors of ecological source areas using two-dimensional spatial data from the land spatial feature data, includes:

[0066] Two-dimensional spatial basic data are extracted from land spatial feature data, and weights are assigned to the two-dimensional spatial basic data indicators that characterize ecological functions. Core spatial units with weight values ​​higher than the set threshold are then selected.

[0067] Integrate adjacent core spatial units with a distance less than a set value, delineate the ecological source area range through boundary fitting, and record the coordinate set and boundary outline of the core area of ​​the source area;

[0068] Based on the coordinate set of the core area of ​​the source region, avoid the special control areas marked in the pre-set two-dimensional space, construct potential connection paths between source regions, and record the spatial direction and width range of the initial corridor.

[0069] In this embodiment, two-dimensional spatial basic data such as a 1:10000 land use status map (land categories classified to secondary classification), ecological function zoning map, and special control area boundary vector data (CGCS2000 National Geodetic Coordinate System, Shapefile format) are extracted from the national land spatial feature data. Four indicators representing ecological functions, namely vegetation coverage, biodiversity index, soil conservation capacity, and water conservation capacity, are selected. The analytic hierarchy process (AHP) is used to assign weights, with the following results: vegetation coverage 0.35, biodiversity index 0.30, soil conservation capacity 0.20, and water conservation capacity 0.15. Spatial units with a total weight value higher than 0.75 are set as core spatial units, and the core spatial units are based on a 20m×20m grid.

[0070] In another embodiment, a spatial proximity analysis method is used to integrate adjacent core spatial units with a distance of less than 500m. The integrated core spatial unit cluster is fitted with a polynomial curve fitting technique to delineate the ecological source area. The coordinate set of the core area of ​​the source area is recorded using latitude and longitude coordinates (accurate to 0.0001°), and the boundary contour is recorded using closed polygon vector data. Based on the coordinate set of the core area of ​​the source area, a 100m avoidance buffer zone is set for the preset special control areas (core area of ​​nature reserve, basic farmland, drinking water source protection area). Potential connecting paths between source areas are constructed using the shortest path algorithm. The path direction is offset within 30° along the tangent direction of the topographic contour line. The initial corridor width is set according to the ecological source area level: 50m when the source area is ≥10km², 30m when it is 5km²-10km², and 20m when it is <5km². The latitude and longitude coordinate sequence and width range data of the initial corridor are recorded simultaneously.

[0071] Optionally, step S4, which verifies the initial corridor based on the three-dimensional connectivity criterion, includes:

[0072] Based on the spatial orientation and width of the initial corridor, and taking into account the uniformity of spatial characteristics, several independent corridor segments are formed, and each segment is assigned a unique spatial identifier.

[0073] The elevation change gradient features and layered permeability features of each corridor segment are extracted segment by segment. The elevation change gradient features, layered permeability features and spatial identifiers of the corridor segments are associated to form a feature dataset for each corridor segment.

[0074] The feature datasets of each corridor segment are compared item by item with the three-dimensional connectivity criteria of the corresponding spatial location. The matching corridor segments that meet the three-dimensional connectivity criteria and the unmatched corridor segments that do not meet the three-dimensional connectivity criteria are marked, and the comparison results are recorded.

[0075] In this embodiment, based on the latitude and longitude coordinate sequence and width range of the initial corridor, and using the terrain slope change ≤5° and vegetation interlayer gap ratio fluctuation ≤10% as the criteria for spatial feature uniformity determination, the initial corridor is divided into several independent corridor segments with lengths of 500m-1000m by a combination of equidistant cutting and feature mutation point cutting. Each corridor segment is assigned a unique spatial identifier consisting of 12 characters (the first 6 characters are the source code, and the last 6 characters are the segment number). The elevation change gradient features (including the maximum slope, average slope, and cumulative slope value within the segment) and layered permeability features (including the tree-shrub layer gap ratio, the shrub-herb layer gap ratio, and the overall permeability mean) of each corridor segment are extracted segment by segment using spatial data extraction technology. These feature data are then bound to the unique spatial identifier of the corridor segment using a key field association method, forming a feature dataset for each corridor segment containing spatial identifier, elevation feature parameters, and permeability feature parameters. The data precision is retained to two decimal places.

[0076] In another embodiment, spatial coordinate precise matching technology is used to locate the three-dimensional connectivity judgment criteria corresponding to each corridor segment. The feature dataset is compared with the judgment criteria item by item in the order of allowable range of elevation adaptation and allowable range of vertical spatial adaptation. When the maximum slope of the corridor segment is ≤ the allowable value of the corresponding gradient level, the average slope is ≤ 80% of the allowable value of the corresponding gradient level, and the average overall permeability is ≥ the allowable value of the corresponding permeability level, it is marked as an adapted corridor segment. Otherwise, it is marked as an unsuitable corridor segment. A comparison result record containing spatial identifier, comparison items, judgment results, and non-compliant parameters is formed. The record format is stored in CSV file and synchronized with the land and space database in real time.

[0077] Optionally, after generating the comparison result record, it also includes:

[0078] Using the two ends of the unsuitable corridor section as fixed anchor points, a rectangular space search area is defined as 1.5 times the initial corridor width, so that the boundary of the rectangular space search area avoids the marked special control area.

[0079] Within the rectangular spatial search area, spatial points that meet the requirements are selected one by one according to the three-dimensional connectivity judgment criteria, and multiple alternative paths are connected to form multiple alternative paths. The spatial curvature and length of each path are calculated.

[0080] The path with the smallest spatial curvature and a length not exceeding 1.2 times that of the original misfit segment is selected as the optimal alternative path, and the coordinate sequence and width range of the optimal alternative path are recorded.

[0081] In this embodiment, the CGCS2000 national geodetic coordinates (accurate to 0.0001°) of the two ends of the mismatched corridor segment are used as fixed anchor points. First, the width data of the original initial corridor (50m, 30m or 20m) is extracted. The short side length of the rectangular spatial search area (corresponding to 75m, 45m or 30m) is calculated by multiplying by 1.5. The long side length is determined by multiplying the straight distance between the two anchor points by 1.5. The direction of the long side of the rectangle is consistent with the direction of the original mismatched corridor segment. Spatial overlay analysis technology is used to perform collision detection between the rectangular area and the vector data of the marked special control areas (core area of ​​nature reserve, basic farmland, drinking water source protection area). For areas with overlap, the rectangular boundary is adjusted by a 50m avoidance distance to ensure that the minimum straight distance between the boundary of the rectangular spatial search area and the boundary of the special control area after adjustment is not less than 50m. Finally, the four corner coordinates and range vector data of the rectangular area are output.

[0082] In one embodiment, within a defined rectangular spatial search area, a set of spatial points covering the entire area is generated using a 30m×30m grid. The 1:50000 digital elevation model (DEM) elevation data and vegetation vertical stratification gap ratio data are extracted for each point. The elevation change gradient (average slope of a single point and its eight adjacent points), the average overall vegetation permeability, and the corresponding three-dimensional connectivity criteria are compared item by item. Valid spatial points are selected based on their elevation change gradient being ≤ the corresponding allowable value and their average overall vegetation permeability being ≥ the corresponding allowable value. These valid spatial points are then clustered by coordinates to form several... For continuous point clusters, a B-spline curve fitting algorithm is used to smoothly connect points within the same cluster according to the proximity principle, forming no less than 5 alternative paths. The coordinates of intermediate nodes are extracted for each path at 10m intervals. The total turning angle within a unit length (100m) is calculated by accumulating the azimuth angle difference between adjacent nodes. Combined with the total path length, the spatial curvature of each alternative path is calculated using the curvature calculation formula (curvature = total turning angle per unit length / 100m) (retained to 3 decimal places). At the same time, the actual path length is calculated using the Haversine formula based on the latitude and longitude difference between each node (accurate to 0.1m).

[0083] In another embodiment, a dual screening rule is set. The first priority is the minimum spatial curvature. All candidate paths are sorted in ascending order of curvature value, and the top 3 paths with the minimum curvature are extracted. The second priority is that the length does not exceed 1.2 times the length of the original unsuitable corridor segment. The actual length of the original unsuitable corridor segment is calculated (accumulated segment by segment according to its coordinate sequence), and the 1.2 times length threshold is checked. The length of the top 3 paths with the minimum curvature is verified, and the path that simultaneously satisfies the minimum curvature and length ≤ the threshold is selected as the optimal alternative path. If the length of the top 3 paths with the minimum curvature exceeds 1.2 times the length of the original unsuitable corridor segment, the path with the minimum curvature and length ≤ the threshold is selected as the optimal alternative path. For the threshold, the path with the length closest to the threshold is selected, and the CGCS2000 latitude and longitude coordinates of the optimal alternative path are collected at 20m intervals to form a complete coordinate sequence. The width range is strictly kept consistent with the original initial corridor width (50m, 30m or 20m). The coordinate sequence and width data are associated with a unique spatial identifier of the corridor segment that does not match the segment (the first 6 digits are the source code and the last 6 digits are the segment number), which is stored in the corridor optimization database. At the same time, the number of effective points, the curvature value and length data of each candidate path, the screening threshold and other intermediate parameters are recorded during the path selection process.

[0084] Importantly, after recording the optimal alternative path, it also includes:

[0085] Following the initial corridor's direction, the adapted corridor segments are aligned with the optimal alternative path at their endpoints, and the coordinate gaps at the connection points are filled in.

[0086] The spatial orientation at the path connection point is smoothed, and the turning angle is adjusted to make the curvature change of the corridor orientation continuous.

[0087] The connectivity of the overall docking path is traversed and verified to check the connection status between the two ends of the path and the boundary of the ecological source area, as well as the conformity status with the three-dimensional connectivity judgment standard of the corresponding area throughout the entire path.

[0088] In this embodiment, the endpoint coordinates of the adapted corridor segment and the starting coordinates of the corresponding optimal alternative path are extracted according to the latitude and longitude coordinate sequence of the initial corridor. The starting coordinates of the adapted corridor segment and the endpoint coordinates of the previous optimal alternative path are also extracted. The endpoint deviation is calculated using the coordinate difference calculation method. When the lateral deviation exceeds 5m or the longitudinal deviation exceeds 3m, coordinate points are added in the deviation interval using the linear interpolation method, with an interval of 2m, until the endpoint coordinates of all adapted corridor segments and the optimal alternative path are completely aligned, filling all coordinate gaps and forming a continuous path coordinate chain.

[0089] In one embodiment, the direction of the coordinate points within a 50m range before and after the path docking point is analyzed. The first azimuth angle formed by the three consecutive coordinate points before the docking point and the second azimuth angle formed by the three consecutive coordinate points after the docking point are calculated. If the difference between the two azimuth angles exceeds 15°, 10 coordinate points are selected on each side of the docking point, and the position of the intermediate coordinate points is adjusted by using cubic polynomial interpolation. This ensures that the gradient change of the turning angle at the docking point is controlled to be no more than 3° every 10m, thus ensuring that the curvature change of the corridor direction is continuous and without abrupt changes.

[0090] In another embodiment, a point-by-point traversal method is used to verify the connectivity of the overall docking path. First, the polygonal vector data of the ecological source area boundary is extracted. Spatial coordinate inclusion analysis is used to determine whether the coordinates of the two ends of the path fall within the ecological source area boundary. If the distance between the endpoint and the source area boundary exceeds 2m, additional coordinate points are added to extend to the source area boundary. Then, the elevation change gradient data and vegetation layer permeability data of the entire path are extracted according to a 30m×30m grid density. The data is compared point by point with the three-dimensional connectivity judgment standard of the corresponding spatial location. The coordinates of the mismatched points and the parameters that do not meet the standard are recorded. If the continuous length of the mismatched points exceeds 100m, the corresponding path segment is re-optimized until the entire path meets the three-dimensional connectivity judgment standard by 100%.

[0091] Preferably, step S5: combining the characteristics of ecological environment status changes, integrating the data of suitable passageways and optimized corridor directions, and outputting a three-dimensional effective corridor network of ecological source areas in the national territory.

[0092] Optionally, step S5 specifically includes:

[0093] Extract spatial range data of ecological environment status anomalies to determine the boundary coordinates and coverage area of ​​the anomaly region;

[0094] The boundary coordinates of the mutated area are spatially overlaid with the data of the adapted passageway and the optimized direction of the passageway, and the proportion of the coverage length of the mutated area in each passageway to the total length of the passageway segment is statistically analyzed segment by segment.

[0095] Based on coverage ratio, the areas are divided into four categories: areas with a coverage ratio of 70% or more are heavily covered areas, areas with a coverage ratio of 30%-70% are moderately covered areas, areas with a coverage ratio of 1%-30% are lightly covered areas, and areas with 0% coverage are uncovered areas.

[0096] In areas with heavy coverage, the entire area is replaced with corridor-optimized routing data. In areas with moderate coverage, only the areas with aberrations in coverage are replaced with the corresponding corridor-optimized routing data. In areas with light coverage and no coverage, the original adapted passageways are retained.

[0097] Edge coordinate matching is performed on corridor data processed by different replacement methods to calibrate topological relationships, process overlapping data and path breakpoints, and form a coherent ecological network.

[0098] In this embodiment, the boundary coordinates (accurate to 0.0001°) and coverage area data (accurate to 0.1km²) of the CGCS2000 national geodetic coordinate system corresponding to the ecological environment status anomaly characteristics are extracted. The boundary coordinates are stored in closed polygon vector format, and the coverage area is calculated point by point based on the boundary coordinates using the polygon area calculation formula. The boundary coordinate vector data of the anomaly area is overlaid with the data of the adapted passageway and the optimized direction of the passageway (including coordinate sequence, width range, and spatial identifier) ​​as layers. The passageway is divided into segments according to the unique spatial identifier of the adapted passageway. The length of the line segment covered by the anomaly area in each passageway unit is counted by the line segment and polygon intersection calculation method. Combined with the total length of the passageway unit (calculated by accumulating the coordinate sequence segment by segment), the coverage ratio is calculated by the formula (anomaly coverage length / total length of passageway unit × 100%) (rounded to the nearest integer).

[0099] In one embodiment, the area is divided into four categories based on the coverage ratio: a coverage ratio ≥ 70% is a heavily covered area, 30% ≤ ratio < 70% is a moderately covered area, 1% ≤ ratio < 30% is a lightly covered area, and 0% is an uncovered area. Each category is assigned a unique identifier code and associated with the corresponding corridor unit. In the heavily covered area, the corridor units are replaced with optimized corridor routing data, and the original corridor width remains unchanged. In the moderately covered area, the corridor segments of the aberrantly covered parts are extracted using spatial coordinate clipping technology and replaced with segments of the same spatial range from the corresponding optimized corridor routing data. The deviation between the endpoints of the replaced segments and the endpoints of the original uncovered parts of the corridor is controlled within 3m. In the lightly covered area and the uncovered area, the coordinate sequence and width data of the original adapted passage corridor are directly retained.

[0100] In another embodiment, edge coordinate matching is performed on all processed corridor data, and the coordinate difference between the endpoints of adjacent corridor segments is calculated. When the lateral or longitudinal deviation exceeds 5m, the coordinate points are supplemented to fill the gap using linear interpolation. For overlapping data, a single valid line segment is retained according to the principle that "corridor optimization direction data takes precedence over the original adapted passage corridor data". For path breakpoints, the shortest distance connection method is used to supplement coordinate points, ensuring that the turning angle at the connection point does not change by more than 3° every 10m. Finally, a coherent ecological network with continuous coordinates, correct topological relationships, and no overlap or breakpoints is formed. The network data is stored in Shapefile format, which includes related fields such as corridor coordinate sequence, width range, area type identifier, and three-dimensional connectivity fit.

[0101] Of particular importance, after the ecological network is formed in step S5, the following also includes:

[0102] Connectivity node detection is performed on the nascent ecological network to extract isolated points in the network, supplement the coordinate data of key connected points, and optimize the network topology.

[0103] The regional dominant species adaptation information corresponding to the three-dimensional connectivity determination criteria is associated with the spatial coordinates of each corridor, and the adaptation species types and corresponding determination criteria parameters are labeled.

[0104] The coordinate data of the ecological source area, the labeled corridor network data, and the boundary coordinates and coverage ratio data of the anomaly impact area are integrated, packaged in a unified spatial data standard format, and output a complete three-dimensional effective corridor network.

[0105] In this embodiment, a node connectivity traversal algorithm is used to detect the initial ecological network. The CGCS2000 coordinates of all corridor nodes in the network are extracted according to a 30m×30m grid density. The number of connecting corridors for each node is counted. Nodes with zero connecting corridors are identified as isolated points. Based on the spatial distance between isolated points and the nearest effective corridor, one key connecting point is added for every 100m increase in distance. The coordinate data of the added points are calculated by linear interpolation. The straight-line distance between the added points and adjacent points does not exceed 50m. The connection paths between isolated points and added points are adjusted so that the number of connecting corridors for all nodes in the network topology is not less than one.

[0106] In one embodiment, the dominant species adaptation information of each region in the three-dimensional connectivity determination criteria is retrieved (including the allowable range of elevation adaptation and the allowable range of vertical spatial adaptation parameters for macaques and leopard cats in forest ecosystems, egrets and otters in wetland ecosystems, and gazelles and foxes in grassland ecosystems). Using precise spatial coordinate matching technology, the dominant species adaptation information is associated with the coordinate sequence of each corridor one by one. The adaptation species type code and the corresponding allowable value of elevation adaptation and allowable value of vertical spatial adaptation are marked in the corridor attribute field. The association accuracy is consistent with the corridor coordinate accuracy (accurate to 0.0001°).

[0107] In another embodiment, the boundary coordinate set of the ecological source area and the core area coordinate data, the labeled corridor network data containing species adaptation information (including coordinate sequence, width range, and regional type identifier), and the boundary coordinates and coverage ratio data of the area affected by changes in the ecological environment status are extracted. The data are encapsulated according to the GB / T28590-2012 geographic information data standard format, and stored in a unified Shapefile vector format. The coordinate system is kept in the CGCS2000 national geodetic coordinate system. The attribute fields include core information such as source area code, corridor identifier, species type, change coverage ratio, and three-dimensional connectivity parameters. The data compression ratio is controlled between 1:5 and 1:8. Finally, complete three-dimensional effective corridor network data is output.

[0108] Preferably, the present invention also provides a land space ecological network identification and optimization system for performing the above-described land space ecological network identification and optimization method, the land space ecological network identification and optimization system comprising:

[0109] The data acquisition module is used to collect ecological and environmental monitoring data and obtain land space characteristic data;

[0110] The ecological anomaly identification module is used to compare ecological environment monitoring data with preset ecological environment benchmark data, identify the characteristics of ecological environment status anomalies, and initiate ecological network structure updates.

[0111] The three-dimensional connectivity establishment module is used to extract topographic elevation information and vegetation vertical structure information from land spatial feature data, and establish three-dimensional connectivity judgment criteria for species mobility adaptation.

[0112] The initial corridor optimization module is used to respond to the update of the ecological network structure. It identifies and determines the initial corridors of the ecological source area through two-dimensional spatial data in the land spatial feature data, verifies the initial corridors based on the three-dimensional connectivity judgment standard, retains the suitable passage corridors and generates corridor optimization direction data.

[0113] The 3D effective corridor network output module is used to combine the characteristics of ecological environment status changes, integrate and match the data of passage corridors and corridor optimization, and output the 3D effective corridor network of the ecological source areas of the national land space.

[0114] Preferably, the present invention also provides a computer storage medium storing a computer program, which, when executed, implements the above-described method for identifying and optimizing the national spatial ecological network.

[0115] Please see Figure 3 The map shows a land area that includes mountains and forests. The lines of different colors correspond to the connecting corridors between ecological source areas: yellow lines are existing corridors that meet the requirements of ecological function adaptation; red lines are the unsuitable corridors in areas where the ecological state has changed; green lines are alternative routes replanned for unsuitable areas. These lines of different colors are connected to each other and together form a network connecting ecological source areas.

[0116] Therefore, the embodiments should be considered 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.

[0117] 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 identifying and optimizing an ecological network of territorial space, characterized in that, Includes the following steps: Step S1: Collect ecological environment monitoring data and obtain land space characteristic data; Step S2: Based on the comparison between the ecological environment monitoring data and the preset ecological environment benchmark data, identify the characteristics of ecological environment status changes and initiate the ecological network structure update. Step S2 includes the following steps: Step S21: Identify the ecosystem types in the ecological environment monitoring data; Step S22: Divide the area into independent zones according to ecosystem type, and configure exclusive ecological and environmental baseline data for each independent zone; Step S23: Split the ecological environment monitoring data into independent zones, and use the time series rolling comparison method to match the exclusive ecological environment benchmark data of the corresponding independent zone at fixed time intervals, and calculate the comparison deviation value for each time period; Step S24: Track the change curve of the comparison deviation value. When the slope of the change curve is continuously positive and the comparison deviation value breaks through the preset stable range, locate the ecological environment change area, simultaneously mark the range and duration of the change, and start the ecological network structure update. Step S3: Extract topographic elevation information and vegetation vertical structure information from the land space feature data to establish a three-dimensional connectivity judgment standard for species mobility adaptation. Step S4: In response to the update of the ecological network structure, the initial corridors of the ecological source area are identified and determined through the two-dimensional spatial data in the land spatial feature data. The initial corridors are verified based on the three-dimensional connectivity judgment criteria, and the adapted passage corridors are retained and corridor optimization direction data is generated. Step S5: Combining the characteristics of ecological environment changes, integrate the data on suitable passageways and optimized corridor directions to output a three-dimensional effective corridor network of ecological source areas in the national territory.

2. The method of claim 1, wherein, Step S3 involves extracting topographic elevation information and vegetation vertical structure information from the land spatial feature data. Three-dimensional spatial basic data are separated from land spatial feature data to extract information on continuous changes in topographic elevation and vertical stratification of vegetation. Calculate the gradient point by point for the continuously changing terrain elevation information, and divide the gradient interval; The vertical stratification structure of vegetation is analyzed to detect interlayer gaps and delineate permeability intervals. Using gradient intervals and transparency intervals as dimensions, a three-dimensional connectivity judgment standard with two-dimensional cross-correlation is established.

3. The method of claim 2, wherein: Step S3, when establishing the three-dimensional connectivity criterion, includes: For gradient intervals, gradient levels are divided by accumulating the slope of continuous spatial segments, and the allowable range of elevation adaptation is defined according to the gradient level. For the permeability range, the permeability level is divided by statistically analyzing the proportion of gaps between each vegetation layer, and the allowable range of vertical space adaptation is defined according to the permeability level. The allowable range of elevation adaptation is spatially superimposed and coupled with the allowable range of vertical space adaptation so that the standard boundary is dynamically associated with the spatial location.

4. The method of claim 1, wherein, Step S4 involves identifying and determining the initial corridors of ecological source areas using two-dimensional spatial data from the land spatial feature data, including: Two-dimensional spatial basic data are extracted from land spatial feature data, and weights are assigned to the two-dimensional spatial basic data indicators that characterize ecological functions. Core spatial units with weight values ​​higher than the set threshold are then selected. Integrate adjacent core spatial units with a distance less than a set value, delineate the ecological source area range through boundary fitting, and record the coordinate set and boundary outline of the core area of ​​the source area; Based on the coordinate set of the core area of ​​the source region, avoid the special control areas marked in the pre-set two-dimensional space, construct potential connection paths between source regions, and record the spatial direction and width range of the initial corridor.

5. The method of claim 4, wherein, Step S4, which verifies the initial corridor based on the three-dimensional connectivity criterion, includes: Based on the spatial orientation and width of the initial corridor, and taking into account the uniformity of spatial characteristics, several independent corridor segments are formed, and each segment is assigned a unique spatial identifier. The elevation change gradient features and layered permeability features of each corridor segment are extracted segment by segment. The elevation change gradient features, layered permeability features and spatial identifiers of the corridor segments are associated to form a feature dataset for each corridor segment. The feature datasets of each corridor segment are compared item by item with the three-dimensional connectivity criteria of the corresponding spatial location. The matching corridor segments that meet the three-dimensional connectivity criteria and the unmatched corridor segments that do not meet the three-dimensional connectivity criteria are marked, and the comparison results are recorded.

6. The method of claim 5, wherein, After the comparison results are recorded, the following are also included: Using the two ends of the unsuitable corridor section as fixed anchor points, a rectangular space search area is defined as 1.5 times the initial corridor width, so that the boundary of the rectangular space search area avoids the marked special control area; Within the rectangular spatial search area, spatial points that meet the requirements are selected one by one according to the three-dimensional connectivity judgment criteria, and multiple alternative paths are connected to form multiple alternative paths. The spatial curvature and length of each path are calculated. The path with the smallest spatial curvature and a length not exceeding 1.2 times that of the original misfit segment is selected as the optimal alternative path, and the coordinate sequence and width range of the optimal alternative path are recorded.

7. The method of claim 1, wherein, Step S5 specifically includes: Extract spatial range data of ecological environment status change characteristics to determine the boundary coordinates and coverage area of ​​the change area; The boundary coordinates of the mutated area are spatially overlaid with the data of the adapted passageway and the optimized direction of the passageway, and the proportion of the coverage length of the mutated area in each passageway to the total length of the passageway segment is statistically analyzed segment by segment. Based on coverage ratio, the areas are divided into four categories: areas with a coverage ratio of 70% or more are heavily covered areas, areas with a coverage ratio of 30%-70% are moderately covered areas, areas with a coverage ratio of 1%-30% are lightly covered areas, and areas with 0% coverage are uncovered areas. In areas with heavy coverage, the entire area is replaced with corridor-optimized routing data. In areas with moderate coverage, only the areas with aberrations in coverage are replaced with the corresponding corridor-optimized routing data. In areas with light coverage and no coverage, the original adapted passageways are retained. Edge coordinate matching is performed on corridor data processed by different replacement methods to calibrate topological relationships, process overlapping data and path breakpoints, and form a coherent ecological network.

8. A system for identifying and optimizing an ecological network of territorial spaces, characterized by the fact that it comprises: For executing the method for identifying and optimizing the territorial spatial ecological network as described in claim 1, the territorial spatial ecological network identification and optimization system comprises: The data acquisition module is used to collect ecological and environmental monitoring data and obtain land space characteristic data; The ecological anomaly identification module is used to compare ecological environment monitoring data with preset ecological environment benchmark data, identify the characteristics of ecological environment status anomalies, and initiate ecological network structure updates. The three-dimensional connectivity establishment module is used to extract topographic elevation information and vegetation vertical structure information from land spatial feature data, and establish three-dimensional connectivity judgment criteria for species mobility adaptation. The initial corridor optimization module is used to respond to the update of the ecological network structure. It identifies and determines the initial corridors of the ecological source area through two-dimensional spatial data in the land spatial feature data, verifies the initial corridors based on the three-dimensional connectivity judgment standard, retains the suitable passage corridors and generates corridor optimization direction data. The 3D effective corridor network output module is used to combine the characteristics of ecological environment status changes, integrate and match the data of passage corridors and corridor optimization, and output the 3D effective corridor network of the ecological source areas of the national land space.

9. A computer storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the method for identifying and optimizing the territorial spatial ecological network as described in any one of claims 1-7.

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