A method for identifying the optimal construction scale for cross-level ecological network construction

Abstract

The present invention discloses an optimal construction scale identification method for cross-level ecological network construction, including: obtaining an initial data set for constructing a cross-level ecological network, and processing it into different granularities to obtain a first data set; using a consistent source identification method, resistance surface setting rules, and corridor and node extraction technology, using the first data set to generate a cross-level ecological network; constructing the spatial dislocation index SMI of ecological sources, corridors, and nodes respectively, and dividing the central urban grid as a calculation unit of the cross-level ecological network; starting from the correlation between scale components and spatial heterogeneity, deriving and analyzing the influence mechanism of SMI scale effect, and designing a scale change experiment of SMI; by changing the expansion amplitude of the central urban area, the granularity ratio of the central urban area and the urban area, and the basic granularity of the central urban area, calculating and changing the SMI before and after, finding the change law and identifying the optimal construction scale of the cross-level ecological network of the urban area and the central urban area of ​​the experimental site; constructing a cross-level ecological network with the optimal construction scale. The present invention provides a basis for the scale selection of multi-level collaborative ecological network construction and provides a reference for regional networked ecological space planning.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological simulation, and relates to a method for constructing a cross-level ecological network, and in particular to a method for identifying an optimal construction scale of a cross-level ecological network. Background Art

[0002] The ecological network is a networked ecological space composed of ecological sources, corridors and nodes that are critical to maintaining the health and safety of ecological processes. It has a stable ecosystem structure and function, can connect various fragmented habitat patches, and realize the normalization of various ecological processes such as species reproduction, diffusion, and migration. It has been widely used in the research and practice of systematic protection of ecological space, such as the construction of regional ecological security pattern.

[0003] However, with the deepening of the combination of research and practice, the rationality of the results of ecological network construction in closed administrative units has begun to be questioned. On the one hand, the ecological source identification method based on static evaluation ignores the impact of landscape connectivity and spatial heterogeneity on ecological functions from a multi-scale dynamic perspective. On the other hand, ecological problems are highly scale-dependent. The ecological network constructed at a single level ignores the scale effect caused by changes in amplitude and granularity, and lacks the exploration of multi-level coordination mechanisms across levels vertically and across boundaries horizontally, which can easily lead to problems such as the key connection areas between ecological network levels being ignored and the dislocation and disconnection between superiors and subordinates.

[0004] Therefore, it is urgent to analyze and summarize the impact of scale on the structure of ecological networks and propose a new method for the optimal scale that can maximize the reduction of the degree of dislocation and disconnection in multi-level ecological networks. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for identifying the optimal construction scale for cross-level ecological network construction. Under the premise of constructing an ecological network spatial dislocation measurement index, this method designs synchronous and asynchronous changes in the network spatial dislocation index with the observation amplitude and data granularity to find the optimal scale that minimizes the degree of cross-level network dislocation, providing a reference for the scale selection of multi-level coupled collaborative ecological network construction.

[0006] The technical solution adopted by the present invention is:

[0007] In a first aspect, the present invention provides a method for identifying an optimal construction scale for cross-level ecological network construction, comprising the following steps:

[0008] An initial data set for constructing a cross-level ecological network is obtained, and the data set is processed into different granularities to obtain a first data set;

[0009] At two different scales, the city and the central urban area, a consistent source identification method, resistance surface setting rules, and corridor and node extraction techniques were used to generate a cross-level ecological network using the first dataset.

[0010] The spatial dislocation index (SMI) of ecological sources, corridors and nodes was constructed respectively, and the central urban area grid was divided as the calculation unit of the cross-level ecological network;

[0011] Starting from the correlation between scale components and spatial heterogeneity, the influencing mechanism of SMI scale effect is deduced and analyzed, and the scale change experiment of SMI is designed;

[0012] By changing the expansion range of the central urban area, the granularity ratio of the central urban area to the city area, and the basic granularity of the central urban area, the SMI before and after the change was calculated to find the change pattern and identify the optimal construction scale of the cross-level ecological network of the city area and the central urban area in the experimental area.

[0013] Constructing a cross-level ecological network with optimal construction scale.

[0014] In a possible implementation manner, the initial data set includes land use data, normalized difference vegetation index and digital elevation model (DEM) data.

[0015] In a possible implementation, the method of generating a cross-level ecological network using the first data set under two different scale perspectives of the city and the central urban area, using a consistent source identification method, resistance surface setting rules, and corridor and node extraction technology, includes the following steps:

[0016] Using the habitat quality module of the InVEST model, we comprehensively considered the relative sensitivity, impact, and distance of habitat types to various threat sources, conducted habitat quality evaluation and grading, and selected high-quality habitat patches as preliminary screening results.

[0017] Based on the preliminary screening results, the area ranking method was used to observe the relationship between the patch area and the number of patches and their contribution to the total area of ​​regional ecological land, and to find the turning point where the contribution rate dropped rapidly as the minimum area threshold, and to conduct secondary screening to obtain candidate patches.

[0018] On the basis of the secondary screening, the dPC values ​​of candidate patches were calculated and divided into five levels according to the natural break method, and the habitat patches with high connectivity importance of levels I to IV were selected as the final ecological source identification results;

[0019] The land use type, terrain index and normalized difference vegetation index were selected as resistance factors to construct the resistance surface;

[0020] Based on the ecological source identification results and the constructed resistance surface, the LinkageMapper tool was used to generate and extract ecological corridors and identify ecological nodes in accordance with the principle of minimum resistance.

[0021] Using the first dataset, the ecological sources, ecological corridors and ecological nodes in the urban area and central urban area at different granularities were identified respectively, and a cross-level ecological network was formed by ecological sources, ecological corridors and ecological nodes at different levels.

[0022] In a possible implementation, the calculation formula of the spatial misalignment index SMI is as follows:

[0023]

[0024] Where SMI represents the spatial dislocation index of ecological sources, corridors and nodes, n is the total number of grid units, p is the total length of small-scale ecological corridors, and p i is the length of the small-scale corridor within the i-unit, A is the total length of the large-scale ecological corridor within the small-scale research range, and A i is the length of the large-scale corridor within unit i.

[0025] In one possible implementation, the scale components include observation amplitude and data granularity.

[0026] Furthermore, starting from the correlation between scale components and spatial heterogeneity, the method of deducing and analyzing the influence mechanism of SMI scale effect and designing the SMI scale change experiment includes the following steps:

[0027] Taking the cross-level ecological network as the analysis object, the control variable method is used to change the data granularity on the basis of keeping the calculation unit size and observation amplitude consistent, draw the scale map of SMI changes, and analyze the scale effect of spatial dislocation of the cross-level ecological network;

[0028] On the basis of maintaining the consistency of basic data granularity and observation amplitude, the size of the SMI measurement grid unit is changed, and a scale map of SMI changes is drawn to analyze the scale effect of spatial dislocation of cross-level ecological networks.

[0029] In a possible implementation, the method of changing the expansion range of the central urban area, the granularity ratio of the central urban area to the city area, and the basic granularity of the central urban area, calculating the SMI before and after the change, finding the change pattern and identifying the optimal construction scale of the cross-level ecological network of the city area and the central urban area in the experimental area includes the following steps:

[0030] The central urban area was expanded outward to different distances. Based on the data within the expansion range, a consistent source identification method, resistance surface setting rules, and corridor and node extraction technology were used to construct cross-level ecological networks (Ens) with different observation ranges. However, the network SMI was still analyzed with the divided central urban area grid as the calculation unit with the change of expansion range to obtain the optimal observation range.

[0031] Maintain the granularity of basic data in the central urban area 0 The city data granularity G 0 Press g 0 The ENs of the city area at different data granularities were obtained by increasing the data granularity by a certain multiple. On this basis, the SMI of the cross-level ecological network between the city area and the central urban area was calculated, and its trend with the granularity ratio was analyzed to obtain the optimal city area data granularity that minimizes the SMI and the corresponding granularity ratio between the city area and the central urban area.

[0032] Maintain the basic data granularity of the city 1 The data granularity of the central urban area is G 1 Press g 1 The ENs of the central urban area at different data granularities were obtained by increasing the data by a certain multiple. On this basis, the SMI of the cross-level ecological network between the central urban area and the city was calculated, and its trend with the granularity ratio was analyzed to obtain the optimal central urban area data granularity that minimizes the SMI and the corresponding granularity ratio between the city and the central urban area.

[0033] Combining the best particle size and particle size ratio results obtained from the two rounds of particle size changes, the best municipal and central urban particle sizes and particle size ratios of the cross-scale ecological network were comprehensively evaluated;

[0034] The optimal observation amplitude and optimal granularity results were combined to obtain the optimal construction scale of the cross-level ecological network in the experimental city and central urban area.

[0035] In a second aspect, the present invention provides an optimal construction scale identification device for cross-level ecological network construction, comprising:

[0036] An acquisition module, used to acquire an initial data set for constructing a cross-level ecological network, and process the data set into different granularities to obtain a first data set;

[0037] The generation module is used to generate a cross-level ecological network using the first dataset at two different scales, namely, the city and the central urban area, using a consistent source identification method, resistance surface setting rules, and corridor and node extraction technology;

[0038] The partitioning module is used to construct the spatial dislocation index (SMI) of ecological sources, corridors and nodes respectively, and to divide the central urban area grid as the calculation unit of the cross-level ecological network;

[0039] The experimental design module is used to deduce and analyze the influence mechanism of SMI scale effect based on the correlation between scale components and spatial heterogeneity, and to design SMI scale change experiments;

[0040] The optimal construction scale search module is used to calculate the SMI before and after the change by changing the expansion range of the central urban area, the granularity ratio of the central urban area and the urban area, and the basic granularity of the central urban area, to find the change pattern and identify the optimal construction scale of the cross-level ecological network of the urban area and the central urban area in the experimental area;

[0041] The final network building module is used to construct a cross-level ecological network with the optimal construction scale.

[0042] In a third aspect, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the aforementioned optimal construction scale identification method for cross-level ecological network construction.

[0043] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned optimal construction scale identification method for cross-level ecological network construction.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The method provided by the present invention constructs a spatial dislocation index (SMI) of ecological sources, corridors and nodes, and applies the above index to ecological simulation, which helps to simulate the degree of dislocation of ecological networks more accurately and objectively, and provides a theoretical and practical basis for the identification of optimal scales across levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is the trend chart of the multi-level ecological network spatial dislocation index change of City A, City B, and City C from 1991 to 2021;

[0047] Figure 2 This is the trend chart of the multi-level ecological network spatial dislocation index of City A, City B, and City C in 2021 with the observation amplitude of the central urban area;

[0048] Figure 3 This is the trend chart of the multi-level ecological network spatial dislocation index of City A, City B, and City C in 2021 as a function of data granularity;

[0049] Figure 4 The scale characteristics of landscape heterogeneity and its mechanism of influence on the spatial dislocation index of multi-level ecological networks;

[0050] Figure 5This is the trend chart of the multi-level ecological network spatial dislocation index of city B in 2021 as a function of data granularity ratio;

[0051] Figure 6 A schematic diagram of the structure of an optimal construction scale identification device system for cross-level ecological network construction provided by an embodiment of the present invention;

[0052] Figure 7 It is a schematic diagram of the physical structure of the electronic device described in the present invention. DETAILED DESCRIPTION

[0053] In order to facilitate ordinary technicians in the field to understand and implement the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0054] A method for identifying the optimal construction scale for cross-level ecological network construction includes the following steps:

[0055] Step 1: Obtain the initial data set for constructing a cross-level ecological network and process it into different granularities to obtain the first data set.

[0056] In a possible implementation manner, the initial data set includes land use data, Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) data.

[0057] For example, this embodiment divides the initial data set into two levels: the city and the central urban area, obtains the land use status, DEM, NDVI and other data required for the construction of the multi-level ecological network, and processes them into data of different granularities. This embodiment selects granularities such as 30, 60, 120, 240, 360, 420, 480, and 600m for experiments.

[0058] Its specific implementation includes the following sub-steps:

[0059] Step 1.1: Use resampling technology to generate land use data of different granularities such as 60, 120, 200, 240, 300, 360, 400, 450, 480, and 600 m in the study area based on the 30 m resolution source land use data;

[0060] Step 1.2: Generate NDVI data of different granularities such as 60, 120, 200, 240, 300, 360, 400, 450, 480, and 600 m in the study area based on the 30 m resolution source NDVI data using resampling technology;

[0061] Step 1.3: Use resampling technology to generate DEM data of different granularities such as 60, 120, 200, 240, 300, 360, 400, 450, 480, and 600 m in the study area based on the 30 m resolution source DEM data, and further generate multi-scale terrain potential index and slope data of the study area through GIS analysis.

[0062] Step 2: At two different scales, the city and the central urban area, a consistent source identification method, resistance surface setting rules, and corridor and node extraction technology are used to generate a cross-level ecological network using the first dataset.

[0063] Step 2 is to eliminate the influence of differences in network construction methods on the scale effect analysis, and its specific implementation includes the following sub-steps:

[0064] Step 2.1: Using the habitat quality module of the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model, the habitat suitability and impact distance of each threat source were comprehensively considered (see Table 1 for details), and habitat quality evaluation and grading were carried out to select high-quality habitat patches as the preliminary screening results.

[0065] Table 1 Habitat suitability and its relative sensitivity to different threats

[0066]

[0067] Note: Habitat suitability is a habitat evaluation standard widely used in wildlife habitat quality assessment.

[0068] In Table 1, habitat types include agricultural land, forest land, shrubs, grassland, water body and maximum impact distance, threat sources include cultivated land, unused land, construction land and roads, and the corresponding numbers of habitat suitability of each habitat type are 0.3, 1.0, 0.7, 0.5, 0.9 and 0.1. The numbers of the intersection of each habitat type to different threat sources are relative sensitivity, such as the relative sensitivity of agricultural land to the threat source of unused land is 0.1; the numbers of the intersection of the maximum impact distance and the threat source are 1.0, 0.5, 5, 5. For example, the number of the intersection of the maximum impact distance and cultivated land is 1.0, which means that the maximum impact distance of cultivated land is 1.0.

[0069] Based on the quality results generated by the habitat quality module in the InVEST model, reclassification was performed in ArcGIS (geographic information software) to obtain five-level habitat quality results, and the patches with the highest two levels of habitat quality were selected as the preliminary screening results.

[0070] Step 2.2: Based on the preliminary screening results, the area ranking method is used to observe the relationship between the patch area and the number of patches and their contribution to the total area of ​​regional ecological land, and to find the inflection point where the contribution rate decreases rapidly as the minimum area threshold, and conduct secondary screening to obtain candidate patches.

[0071] Step 2.3: Based on the secondary screening, the dPC value of the candidate patches was calculated using Confor26 software (Formula 1), which was divided into five levels according to the natural break method. Habitat patches with high connectivity importance of levels I to IV were selected as the final ecological source identification results.

[0072] (1)

[0073] Among them, PC is the possible connectivity index, dPC is the rate of change of the connectivity probability index, is the possible connectivity index of the removed source.

[0074] Step 2.4: Select land use type, topographic potential index (TPI) and NDVI as resistance factors to construct the resistance surface. TPI is an index that comprehensively reflects the surface features such as elevation and slope.

[0075] Furthermore, the Delphi method was used to collect evaluation weight tables from relevant experts, determine the relative resistance coefficients and weights to construct a resistance factor system. The evaluation weight table is shown in Table 2, and the ecological resistance factor coefficients and weights are shown in Table 3.

[0076] Table 2 Expert evaluation weight table

[0077]

[0078] Note: 5 - very important; 4 - important; 3 - average; 2 - not important; 1 - very unimportant

[0079] Table 3 Ecological resistance factor coefficients and weights

[0080]

[0081] Step 2.5: Based on the resistance surface identified and constructed from the ecological source, follow the principle of minimum resistance, use the Linkage Mapper tool designed based on "circuit theory" to extract ecological corridors and identify ecological nodes (pinch points, barrier points). "Pinch points" are key locations that can prevent habitat degradation and need to be protected, and barrier points are areas that pose a greater obstacle to species migration between patches.

[0082] Step 2.6: Using the first dataset, identify the ecological sources, ecological corridors and ecological nodes in the city and central urban area at different granularities, and form a cross-level ecological network composed of ecological sources, ecological corridors and ecological nodes at different levels.

[0083] Step 3: Construct the spatial dislocation index (SMI) of ecological sources, corridors, and nodes respectively, and divide the central urban area grid as the calculation unit of the cross-level ecological network.

[0084] In one possible implementation, the small-scale study area (central urban area) is divided into n homogeneous computational grids, and the spatial dislocation index (SMI) of ecological sources, corridors and nodes is constructed respectively (including: SMI _source 、SMI _corridor and SMI _nodes ), the specific formula is as follows:

[0085] (2)

[0086] In the formula, the meaning of the parameters is the corridor dislocation index SMI_ corridor For example, n is the total number of grid units, p is the total length of the small-scale ecological corridor, and p i is the length of the small-scale corridor within the i-unit, A is the total length of the large-scale ecological corridor within the small-scale research range, and A i is the length of the large-scale corridor in unit i. The spatial dislocation index SMI_ of the source can be obtained by replacing the meaning of the parameters related to the corridor length with the source area and the number of nodes. source and node misalignment index SMI_ nodes The larger the SMI value, the more serious the spatial dislocation and disconnection of the ecological network between levels.

[0087] Step 4: Starting from the correlation between scale components and spatial heterogeneity, deduce and analyze the influencing mechanism of SMI scale effect and design the SMI scale change experiment.

[0088] In one possible implementation, the scale components include observation amplitude and data granularity.

[0089] Further, the step 4 includes the following sub-steps:

[0090] Step 4.1: Taking the cross-level ecological network as the analysis object, the control variable method was used to change the basic data granularity (30-600 m) on the basis of keeping the measurement grid unit size and the expansion range of the observation angle consistent, and to draw a scale map of SMI changes to analyze the scale effect of spatial dislocation of the ecological network.

[0091] Step 4.2: On the basis of maintaining the consistency of the basic data granularity and the expansion range of the observation perspective, change the size of the SMI measurement grid unit, draw a scale map of the SMI change, and analyze the scale effect of the spatial dislocation of the ecological network.

[0092] Step 5: By changing the expansion range of the central urban area, the granularity ratio of the central urban area to the urban area, and the basic granularity of the central urban area, calculate the SMI before and after the change, find the change pattern and thereby identify the optimal construction scale of the ecological network of the urban area and central urban area in the experimental area.

[0093] In one possible implementation, step 5 includes the following sub-steps:

[0094] Step 5.1: Expand the central urban area outward to different distances, and build ecological networks (Ens) with different observation ranges based on the data within the expansion range, using consistent source identification methods, resistance surface setting rules, and corridor and node extraction techniques. However, the changes in network SMI with the expansion range are still analyzed using the divided central urban area grids as calculation units to obtain the optimal observation range.

[0095] Step 5.2: Maintain the granularity of the basic data of the central urban area 0 The city data granularity G 0 Press g 0 The city-wide ecological network (Ens) at different data granularities is obtained by increasing the data by a certain multiple. On this basis, the SMI of the city-wide ecological network and the central urban area is calculated, and the trend of its change with the granularity ratio is analyzed to obtain the optimal city-wide data granularity that minimizes the SMI and the corresponding granularity ratio between the city-wide and central urban areas.

[0096] Step 5.3: Maintain the basic data granularity g of the city 1 The data granularity of the central urban area is G 1 Press g 1 The ecological network of the central urban area (Ens) under different data granularities is obtained by increasing the data by a certain multiple. On this basis, the SMI of the central urban area and the cross-level ecological network of the city are calculated, and the trend of its change with the granularity ratio is analyzed to obtain the optimal central urban area data granularity that minimizes the SMI and the corresponding granularity ratio between the city and the central urban area.

[0097] Step 5.4: Combine the best particle size and particle size ratio results obtained from the two rounds of particle size changes to comprehensively evaluate the best municipal and central urban particle sizes and particle size ratios for the cross-scale ecological network;

[0098] Step 5.5: Combine the optimal observation amplitude and the optimal granularity results to obtain the optimal construction scale of the cross-level ecological network in the experimental city and central urban area.

[0099] Step 6: Construct a cross-level ecological network with the optimal construction scale.

[0100] In one possible implementation method, the method for constructing a cross-level ecological network with an optimal construction scale is: using Visual C# as the development platform, using ArcGIS to establish a spatial database and an attribute database, adopting integrated secondary development technology, and using ArcGIS's Arc Engine development component to implement multi-level ENs (ecological network) generation technology, cross-level ENs spatial dislocation measurement algorithm, and ENs spatial dislocation multi-scale analysis algorithm integration in C# to construct a cross-level ecological network with an optimal construction scale.

[0101] Furthermore, the specific construction method includes the following sub-steps:

[0102] Step 6.1: Based on the organization, management and call relationship of data, ArcGIS Desktop, ArcSDE and Oracle10G are used to store and manage the spatial data and attribute data involved in the construction of the ecological network, and to realize the integrated graphic and text management function of the basic data of the ecological network.

[0103] Step 6.2: Build a plug-in GIS (geographic information system) application framework: design a framework pattern to generate a plug-in interface; develop a plug-in identification module to identify plug-ins belonging to the system; design a communication contract so that the plug-in can interact with the host program.

[0104] Step 6.3: Install and import the ArcEngine development component of ArcGIS, use ArcEngine controls to build the host program, and use ArcEngine development components to implement related GIS functions and insert them into the host program in the form of plug-ins. Use modular ideas and hierarchical component models to integrate business function modules and subsystems, build a multi-level ENs generation technology, cross-level ENs spatial dislocation measurement algorithm, and ENs spatial dislocation multi-scale analysis algorithm integration platform to achieve computerization of ecological network generation, SMI measurement, data storage, analysis, query, output and transmission, and meet the needs of optimal construction scale identification of cross-level ecological networks.

[0105] This will be described below in conjunction with more specific embodiments.

[0106] In this example, cities A, B and C are selected as experimental areas. The experimental data mainly include land use data of the experimental area at intervals of 10 years from 1991 to 2021, digital elevation model (DEM) of the experimental area, and NDVI data of the experimental area at intervals of 10 years from 1991 to 2021. The land use raster data is preprocessed based on ArcGIS 10.8 software (resampled to obtain data of different granularities), and then combined with InVEST and conefor26 platforms, based on a unified multi-level ecological network (ENs) construction method, the ENs of the city and central urban area of ​​city A, city B, and city C in 1991, 2001, 2011, and 2021 are generated.

[0107] At the city scale, the basic layout of the core patches of the ecological sources of the three cities remained unchanged from 1991 to 2021, while the area showed a decreasing trend; at the scale of the central urban area, the scale of the ecological sources and overlapping sources in the study area decreased from 1991 to 2011, and then increased slightly, which is basically consistent with the urbanization process of the three cities. The more than 20 years since 1991 were the golden period of rapid economic and urban construction development. Along with the rapid urbanization process, the intensive growth of construction land in the central urban area and the squeezing and encroachment of ecological land have caused the ecological sources to shrink continuously. In the past decade, with the popularization of the concept of ecological civilization and the improvement of the ecological protection system, the trend of ecological degradation in the study area has been curbed, and the protection of key species habitats has been continuously strengthened. At the same time, as a channel for species migration between sources, the distribution of corridors is related to the layout of sources and resistance surfaces. For example, City A is a city with a centralized layout, in which the central construction land is highly dense and the ecological resistance value is high. The city's ecological sources are generally scattered around the central urban area. Therefore, except for the main axis of the Yangtze River that runs through the city, the urban ecological corridors basically bypass the central urban area, forming a ring-shaped distribution pattern. City B is a typical city with a strip-shaped decentralized layout. Due to the terrain restrictions, the east-west urban construction strip is separated by the ecological landscape strip. Therefore, the urban corridor is mainly to achieve horizontal extension and vertical connection between ecological landscape strips, and is relatively evenly distributed throughout the city.

[0108] Furthermore, the central urban area of ​​the study area was divided into a homogeneous grid of 10 km × 8 km, and the constructed ecological network spatial dislocation index was used to calculate the SMI of ecological sources, corridors and nodes in the three cities in the four periods from 1991 to 2021. The results are as follows: Figure 1As shown. It can be seen that the trends of network SMI in different study areas over time are different. The spatial dislocation degree of the source of city A has continued to increase over time, from 0.051 in 1991 to 0.502 in 2021; the SMI_source of city B fluctuates between 0.1 and 0.3; and city C basically remains at around 0.13. The spatial dislocation degree of ecological corridors and nodes in cities A and C shows a floating trend of decline-increase-and then decline over time, while the SMI_corridor and SMI_nodes of city B show a continuous upward trend. At the same time, the correlation of SMI of different network elements is inconsistent. SMI_corridor and SMI_nodes are highly correlated, and their changing trends are basically consistent, while the correlation with SMI_source is not obvious.

[0109] Combining the multi-level ENs distribution results of each study area and Figure 1 It can be seen that the constructed ecological network SMI can accurately reflect the degree of dislocation and disconnection of the spatial structure of the two-level ecological network. For example, from 1991 to 2021, the urban physical area of ​​City A continued to expand, causing the habitat quality of the ecological patches in the central urban area to decline, and it was no longer identified as a city-wide source, resulting in a decrease in the two-level overlapping sources and an increase in SMI. For example, in 1991, an overlapping ecological source on the southwest coast of City B was lost due to subsequent coastal development and construction activities, resulting in a large dislocation of the two-level corridors in the southwest, and SMI_corridor increased from 0.269 in 1991 to 0.546 in 2021. In addition, the figure also explains the correlation between the SMIs of the three types of network elements. Since the ecological nodes are all located on the corridor or not far from the surrounding area, the SMIs of the two are highly consistent. The source is different. City B and City C have long maintained a source overlap of more than 70% and a low SMI_source, but the SMIs of the corridors and nodes in the two places have always been at a high level. The reason is that most of the overlapping sources are located at the edge of the central urban area, which is formed by the division of the urban source areas by the central urban area boundary. The research perspectives based on corridor extraction are different, and the connection starting points and shortest cost paths between the sources are different, so there is little correlation between the source and corridor SMI.

[0110] Amplitude is the continuous range or length of the research object in the space-time dimension. The difference in the analysis amplitude of the two-level ENs leads to differentiated source selection results, which in turn affects the layout of corridors and nodes, resulting in the spatial dislocation phenomenon of ENs. The observation amplitude on which the construction of the central urban area ENs is based will affect the degree of spatial dislocation. In order to quantitatively study the form and law of this influence, on the basis of the 2021 urban ENs of the study area, the central urban area is expanded outward to different distances, and based on the data within the expansion range, a consistent source identification method, resistance surface setting rules, and corridor and node extraction technology are used to construct ENs results with different observation amplitudes. Furthermore, the divided central urban area range grid is still used as the basic unit for SMI calculation, and the trend of SMI of the multi-level ecological network of the three cities in 2021 with amplitude is obtained as shown below. Figure 2 shown.

[0111] Combining the ENs results with different observation amplitudes Figure 2 It can be seen that under the same data granularity and ENs construction method, with the expansion of the observation range, the overlapping area of ​​the two-level ecological source areas in the study area gradually increases, the spatial distribution gradually converges, and the degree of spatial dislocation of ENs generally shows a step-by-step downward trend. When the observation range is extended to the entire city, SMI will return to zero, and the spatial dislocation phenomenon will no longer exist. At the same time, there are multiple sudden inflection points in the process of SMI decline. The amplitude at which the inflection points of the three types of network elements SMI appear simultaneously is called the critical amplitude. The scale and proportion of the city and central urban area of ​​different cities are different, and the critical amplitude is also different. For example, 5km and 12km are the critical amplitudes of urban ASMI changes, while the corresponding amplitudes of city B and city C are 30km and 50km respectively. The critical amplitude is of great significance and can help decision makers better understand the characteristic scale of regional ecosystems. When constructing multi-level ENs, the optimal observation and analysis amplitude should be selected to achieve the best dislocation elimination effect with the smallest possible amplitude change.

[0112] Granularity is the size of the smallest recognizable unit in the landscape. Both data granularity and SMI calculation grid granularity will affect the spatial dislocation of ENs. The trend of ecological network SMI changing with data granularity is shown in Figure 2. Figure 3As shown in the figure, the SMI of the three types of network elements in the study area fluctuates around the initial value with the change of data granularity. Except that the SMI fluctuation trend of corridors and nodes remains basically consistent, there is no obvious regularity. The reason is related to the scale characteristics of landscape heterogeneity. The heterogeneity of regional landscape determines the distribution of source and resistance surface to a certain extent, and has a great impact on the structure, layout and dislocation of multi-level ENs. The greater the difference in spatial heterogeneity between levels, the greater the SMI. Generally speaking, spatial heterogeneity decreases with the increase of granularity. Since the ecosystem is a complex chaotic system, the decrease in heterogeneity of ecological space at different levels with the change of granularity has random and nonlinear characteristics. Therefore, the change result of the heterogeneity difference between the two levels cannot be determined, which makes the final SMI show a non-periodic up and down floating state with the trend of granularity change.

[0113] At the same time, in order to explore the impact of grid granularity on the results, based on the ENs generated by City A and City B at a resolution of 30m in 2021, the grid granularity was set to 4 / 5, 3 / 5, 1 / 2, 2 / 5, and 1 / 4 of the original grid size (10km×8km) in turn, and the change results are shown in Table 4. It can be seen that as the grid size decreases and the number increases, the SMI of the three types of elements shows an overall increasing trend, among which the changes in the corridor and node SMI are more regular. Its increase gradually decreases with the increase in the number of grids, and eventually tends to be stable; with the increase in grid size, it shows a faster downward trend. It can be inferred that when the grid size increases to a certain extent, the SMI will become 0. Therefore, in actual research, the choice of grid size will have a greater impact on the results and must be taken into consideration.

[0114] Table 4 Changes of SMI of ecological network of city A and city B with the size of computational grid

[0115]

[0116] Based on the above analysis results, it can be seen that the scale of landscape heterogeneity has a great influence on the occurrence and degree of spatial dislocation of multi-level ENs. Heterogeneity can promote, hinder or interfere with the flow and propagation of regional species, substances and energy in the landscape, thereby affecting the structure of ENs. Therefore, it can be concluded that the mechanism by which scale affects the spatial dislocation of cross-level ENs through heterogeneity is as follows: Figure 4 shown.

[0117] Figure 4 (a) and Figure 4 (b) in the figure shows the influence of the observed amplitude and granularity on spatial heterogeneity. It can be seen that heterogeneity often increases with the increase of amplitude, while with the increase of granularity, the heterogeneity between units will weaken. Assuming that A and B in the figure are the two amplitude ranges of the central urban area and the urban area, respectively, the initial heterogeneity difference is a certain initial value △D0 Without considering the changes in granularity and methods, when the research scope of the central urban area expands to A', the heterogeneity difference between it and the urban area is reduced to △D1. The reduction of the heterogeneity gap between the two levels causes the reduction of the degree of cross-level ENs misalignment, which explains the law of change of SMI in the study area with the scope in the figure.

[0118] Figure 4 (c) in Figure 4 (a) and Figure 4 Combined with the law reflected in (b) in the figure, it shows the influence of granularity on the relationship between heterogeneity and amplitude. It can be seen that when the amplitude is constant, as the granularity increases, the spatial heterogeneity of each amplitude decreases, and the curve moves downward as a whole, and vice versa. Taking 30m resolution as an example, the landscape heterogeneity corresponding to the urban area and the central urban area is b0 and a0, respectively. With the synchronous increase of the data granularity of the two-level study areas, the curve moves downward, and the corresponding heterogeneity decreases to b1 and a1, respectively. Due to the complexity and nonlinearity of the ecosystem and its scale variation characteristics, the size relationship between b0-a0 and b1-a1 cannot be determined, which explains the following. Figure 5 The disorder of the ecological network SMI changes with the granularity shown in Figure 2. Figure 4 In (c), if the data granularity of the city and the central urban area is further changed asynchronously, the data granularity used at A is kept unchanged, and the basic data granularity at B is increased, a new heterogeneity difference b1-a0 can be obtained. Obviously, this value is smaller than the initial value b0-a0, and the corresponding SMI will also decrease in theory at this time, indicating that the network SMI is likely to have a certain regularity with the asynchronous change of the two-level data granularity. It is worth further exploring the manifestation of this regularity and finding the critical granularity that minimizes the SMI.

[0119] In order to find the ecological network scale and amplitude that minimizes SMI, the experiment takes city B as an example and maintains the basic data granularity g in the central urban area. 0 The granularity G of the urban data is set to g 0 A certain multiple increase to obtain different data granularity (G 1 , G 2 , G 3 , G n ..., n is the increase multiple), and on this basis, the SMI of the ecological network of the central urban area is calculated as follows: Figure 5 As shown in the figure, it can be seen that as the granularity of the city data increases, the SMI value shows an overall trend of first increasing, then fluctuating slightly, and finally increasing significantly. Figure 5 As can be seen in (a), the particle size ratio is relatively low at 1 / 10 and 1 / 15, while Figure 5It can be seen from (b) that when the central urban area is 60m granularity, the granularity ratio is relatively low at 1 / 6 and 2 / 15. It is worth noting that when the city area adopts 450m granularity, the corresponding central urban area basic data granularity g 0 The SMI of 30m and 60m both showed a significant decrease. Therefore, according to the trend of the granularity ratio change, it can be considered that when the central urban area is 30m or 60m granularity, the optimal granularity of the urban area is 450m. Figure 5 As can be seen in (b), with the expansion of the observation amplitude, the overlapping area of ​​the ecological source areas between the urban area and the central urban area of ​​City B gradually increases, and the spatial distribution gradually converges. The degree of spatial misalignment of ENs generally shows a step-by-step downward trend, and there is a significant decrease at 5km and 30km. Therefore, considering the principle of minimizing the expansion amplitude to minimize the SMI, it can be considered that the optimal amplitude of the ecological network for City B that minimizes the SMI in 2021 is 5km.

[0120] The above analysis verifies that this embodiment constructs a method that reasonably considers the scale effect of spatial dislocation of network structure and effectively identifies the optimal construction scale of cross-level ecological networks. It explores the regularity of SMI at two scales and finds the critical granularity that minimizes SMI, which provides a more objective and accurate theoretical basis and practical operation process for identifying the optimal construction scale of cross-level ecological networks. It also reduces the repetitive operations of constructing multi-scale, multi-granularity and multi-amplitude ecological networks by constructing a multi-platform algorithm collection system, and provides a more free and convenient platform support for subsequent scale expansion research and practical applications in more regions, thereby reducing the burden and improving the efficiency of constructing a cross-level ecological network with the optimal scale.

[0121] The optimal construction scale identification device for cross-level ecological network construction provided by the present invention is described below. The optimal construction scale identification device for cross-level ecological network construction described below and the optimal construction scale identification method for cross-level ecological network construction described above can be referenced to each other.

[0122] Figure 6 is a structural diagram of an optimal construction scale identification device system for cross-level ecological network construction provided by an embodiment of the present invention, such as Figure 6 As shown, it includes: an acquisition module 61, a generation module 62, a division module 63, an experimental design module 64, an optimal construction scale search module 65 and a final network construction module 66, wherein:

[0123] An acquisition module 61 is used to acquire an initial data set for constructing a cross-level ecological network, and process the initial data set into different granularities to obtain a first data set;

[0124] A generation module 62 is used to generate a cross-level ecological network using the first data set under two different scale perspectives, namely, the city area and the central urban area, by using a consistent source identification method, resistance surface setting rules, and corridor and node extraction technology;

[0125] A partitioning module 63 is used to construct the spatial dislocation index SMI of ecological sources, corridors and nodes respectively, and to divide the central urban area grid as the calculation unit of the cross-level ecological network;

[0126] Experimental design module 64 is used to deduce and analyze the influence mechanism of SMI scale effect based on the correlation between scale components and spatial heterogeneity, and design SMI scale change experiments;

[0127] The optimal construction scale search module 65 is used to calculate the SMI before and after the change by changing the expansion range of the central urban area, the granularity ratio of the central urban area and the urban area, and the basic granularity of the central urban area, to find the change law and identify the optimal construction scale of the cross-level ecological network of the urban area and the central urban area in the experimental area;

[0128] The final network construction module 66 is used to construct a cross-level ecological network with the optimal construction scale.

[0129] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7 As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730 and a communication bus 740, wherein the processor 710, the communication interface 720 and the memory 730 communicate with each other through the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute an optimal construction scale identification method for cross-level ecological network construction.

[0130] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0131] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute an optimal construction scale identification method for cross-level ecological network construction provided by the above methods.

[0132] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the optimal construction scale identification for a cross-level ecological network construction provided by the above methods.

[0133] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0134] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying the optimal construction scale for cross-level ecological network construction, It is characterized in that The following steps are involved: An initial data set for constructing a cross-level ecological network is obtained, and the data set is processed into different granularities to obtain a first data set; At two different scales, the city and the central urban area, a consistent source identification method, resistance surface setting rules, and corridor and node extraction techniques were used to generate a cross-level ecological network using the first dataset. The spatial dislocation index (SMI) of ecological sources, corridors and nodes was constructed respectively, and the central urban area grid was divided as the calculation unit of the cross-level ecological network; Starting from the correlation between scale components and spatial heterogeneity, the influencing mechanism of SMI scale effect is deduced and analyzed, and the scale change experiment of SMI is designed; By changing the expansion range of the central urban area, the granularity ratio of the central urban area to the city area, and the basic granularity of the central urban area, the SMI before and after the change was calculated to find the change pattern and identify the optimal construction scale of the cross-level ecological network of the city area and the central urban area in the experimental area. Constructing a cross-level ecological network with optimal construction scale.

2. The optimal scale identification method for cross-level ecological network construction according to claim 1, It is characterized in that The initial data set includes land use data, normalized difference vegetation index and digital elevation model data.

3. The optimal scale identification method for cross-level ecological network construction according to claim 1, It is characterized in that The method of generating a cross-level ecological network using the first data set under two different scale perspectives of the city and the central urban area, using a consistent source identification method, resistance surface setting rules, and corridor and node extraction technology, includes the following steps: Using the habitat quality module of the InVEST model, the habitat suitability, impact and distance factors of each threat source were comprehensively considered to evaluate and grade the habitat quality, and high-quality habitat patches were selected as the preliminary screening results. Based on the preliminary screening results, the area ranking method was used to observe the relationship between the patch area and the number of patches and their contribution to the total area of ​​regional ecological land, and to find the turning point where the contribution rate dropped rapidly as the minimum area threshold, and to conduct secondary screening to obtain candidate patches. On the basis of the secondary screening, the dPC values ​​of candidate patches were calculated and divided into five levels according to the natural break method, and the habitat patches with high connectivity importance of levels I to IV were selected as the final ecological source identification results; The land use type, terrain index and normalized difference vegetation index were selected as resistance factors to construct the resistance surface; Based on the ecological source identification results and the constructed resistance surface, the Linkage Mapper tool was used to generate and extract ecological corridors and identify ecological nodes in accordance with the principle of least resistance. Using the first dataset, the ecological sources, ecological corridors and ecological nodes in the urban area and central urban area at different granularities were identified respectively, and a cross-level ecological network was formed by ecological sources, ecological corridors and ecological nodes at different levels.

4. The optimal scale identification method for cross-level ecological network construction according to claim 1, It is characterized in that The calculation formula of the spatial dislocation index SMI is as follows: Where SMI represents the spatial dislocation index of ecological sources, corridors and nodes, n is the total number of grid units, p is the total length of small-scale ecological corridors, and p i is the length of the small-scale corridor within the i-unit, A is the total length of the large-scale ecological corridor within the small-scale research range, and A i is the length of the large-scale corridor within unit i.

5. The optimal scale identification method for cross-level ecological network construction according to claim 1, It is characterized in that The scale components include observation amplitude and data granularity.

6. The optimal scale identification method for cross-level ecological network construction according to claim 5, It is characterized in that The method of deducing and analyzing the influence mechanism of SMI scale effect based on the correlation between scale components and spatial heterogeneity and designing the SMI scale change experiment includes the following steps: Taking the cross-level ecological network as the analysis object, the control variable method is used to change the data granularity on the basis of keeping the calculation unit size and observation amplitude consistent, draw the scale map of SMI changes, and analyze the scale effect of spatial dislocation of the cross-level ecological network; On the basis of maintaining the consistency of the basic data granularity and the expansion range of the observation perspective, the size of the SMI measurement grid unit is changed, and a scale map of the SMI changes is drawn to analyze the scale effect of spatial dislocation of cross-level ecological networks.

7. The optimal scale identification method for cross-level ecological network construction according to claim 5, It is characterized in that The method of changing the expansion range of the central urban area, the granularity ratio of the central urban area to the city area, and the basic granularity of the central urban area, calculating the SMI before and after the change, finding the change pattern and identifying the optimal construction scale of the cross-level ecological network of the city area and the central urban area in the experimental area, includes the following steps: The central urban area was expanded outward to different distances. Based on the data within the expansion range, a consistent source identification method, resistance surface setting rules, and corridor and node extraction technology were used to construct cross-level ecological networks with different observation ranges. However, the changes of network SMI with the expansion range were still analyzed using the divided central urban area grids as calculation units to obtain the optimal observation range. Maintain the granularity of basic data in the central urban area 0 The city data granularity G 0 Press g 0 The urban ecological network under different data granularities is obtained by increasing the data by a certain multiple. On this basis, the SMI of the cross-level ecological network between the urban area and the central city is calculated, and its trend with the granularity ratio is analyzed to obtain the optimal urban data granularity with the minimum SMI and the corresponding granularity ratio between the urban area and the central city. Maintain the basic data granularity of the city 1 The data granularity of the central urban area is G 1 Press g 1 The ecological network of the central urban area under different data granularities is obtained by increasing the data by a certain multiple. On this basis, the SMI of the ecological network across levels of the city is calculated, and its trend with the granularity ratio is analyzed to obtain the optimal central urban area data granularity that minimizes the SMI and the corresponding granularity ratio between the city and the central urban area. Combining the best particle size and particle size ratio results obtained from the two rounds of particle size changes, the best municipal and central urban particle sizes and particle size ratios of the cross-scale ecological network were comprehensively evaluated; The optimal observation amplitude and optimal granularity results were combined to obtain the optimal construction scale of the cross-level ecological network in the experimental city and central urban area.

8. An optimal construction scale identification device for cross-level ecological network construction, It is characterized in that include: An acquisition module, used to acquire an initial data set for constructing a cross-level ecological network, and process the data set into different granularities to obtain a first data set; The generation module is used to generate a cross-level ecological network using the first dataset at two different scales, namely, the city and the central urban area, using a consistent source identification method, resistance surface setting rules, and corridor and node extraction technology; The partitioning module is used to construct the spatial dislocation index (SMI) of ecological sources, corridors and nodes respectively, and to divide the central urban area grid as the calculation unit of the cross-level ecological network; The experimental design module is used to deduce and analyze the influence mechanism of SMI scale effect based on the correlation between scale components and spatial heterogeneity, and to design SMI scale change experiments; The optimal construction scale search module is used to calculate and change the SMI before and after the change by changing the expansion range of the central urban area, the granularity ratio of the central urban area and the urban area, and the basic granularity of the central urban area, to find the change pattern and identify the optimal construction scale of the cross-level ecological network of the urban area and the central urban area of ​​the experimental site; The final network building module is used to construct a cross-level ecological network with the optimal construction scale.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the program, the optimal construction scale identification method for cross-level ecological network construction as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the optimal construction scale identification method for cross-level ecological network construction as claimed in any one of claims 1 to 7 is implemented.

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