Method and system for constructing ecological safety pattern of black soil area in high-latitude cold region

Abstract

The invention discloses a construction method and system for an ecological security pattern of a high-latitude cold region black soil area, and the method comprises the steps: recognizing a comprehensive ecological source land of the high-latitude cold region black soil area based on an InVEST model and landscape form spatial pattern analysis; constructing a multi-dimensional resistance surface based on the identified comprehensive ecological source land; based on the constructed resistance surface, extracting an ecological corridor; the extracted ecological corridor is optimized, and construction of the ecological safety pattern of the black soil area in the cold region is completed. According to the method, factors such as seasonal change, natural resistance factors and ecological corridor width optimization are comprehensively considered, a solution which is more flexible and high in adaptability is provided, and the stability and sustainability of the ecological safety pattern of the black soil area in the high-latitude cold region can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment protection, and particularly relates to a method and system for constructing an ecological security pattern in the high-latitude cold black soil area. Background Art

[0002] With the intensification of global climate change, the stability and health of ecosystems are facing increasing challenges, especially in the high-latitude cold black soil area. These areas have unique climate and ecological characteristics and are extremely sensitive to climate change, so their ecological security issues are particularly important. However, the existing research on ecological security pattern (ESP) mainly focuses on ecological sensitive areas such as urban agglomeration areas and karst areas, and there is a lack of application in the high-latitude cold black soil area.

[0003] At present, many ecological security patterns do not consider seasonal factors such as the number of snow cover days when designed, resulting in poor connectivity of ecological corridors in winter and cold climate conditions. The traditional ecological security model ignores the influence of natural resistance factors such as snow, ice, and frozen soil, and cannot effectively support the seasonal migration of species in the high-latitude cold black soil area. In addition, the width of ecological corridors is crucial for the stability and functionality of ecological networks, but the research on optimizing the corridor width is still relatively limited.

[0004] The existing technology usually constructs a resistance surface based on land use and vegetation information, and fails to effectively respond to the impacts brought by seasonality and climate change. With the deterioration of the ecological environment in the high-latitude cold black soil area, the traditional method faces limitations and fails to provide sufficient adaptability and flexibility, and cannot effectively support the survival of species and the long-term sustainable development of ecosystems.

[0005] In summary, there are obvious deficiencies in the existing technology in constructing the ecological security pattern in the high-latitude cold black soil area, especially in terms of the influence of seasonal factors, natural resistance factors, and the optimization of ecological corridor width. Summary of the Invention

[0006] To solve the above technical problems in the background, the present invention designs a method for constructing an ecological security pattern in the high-latitude cold black soil area based on multi-scenario optimization and ecological network coupling. By comprehensively considering factors such as seasonal changes, natural resistance factors, and the optimization of ecological corridor width, the present invention provides a more flexible and adaptable solution, which can effectively improve the stability and sustainability of the ecological security pattern in the high-latitude cold black soil area.

[0007] To achieve the above object, the present invention provides a method for constructing an ecological security pattern in the high-latitude cold black soil area, and the steps include:

[0008] Identify the comprehensive ecological source areas in the high-latitude cold black soil region based on the InVEST model and landscape morphological spatial pattern analysis;

[0009] Construct a multi-dimensional resistance surface based on the identified comprehensive ecological source areas;

[0010] Extract ecological corridors based on the constructed resistance surface;

[0011] Optimize the extracted ecological corridors to complete the construction of the ecological security pattern in the cold black soil region.

[0012] Preferably, the method for identifying the comprehensive ecological source areas includes: identifying the functional ecological source areas and structural ecological source areas in the high-latitude cold black soil region through the InVEST model and landscape morphological spatial pattern analysis, and constructing the comprehensive ecological source areas through spatial overlay.

[0013] Preferably, the method for constructing the multi-dimensional resistance surface includes: combining human interference and natural constraint factors to simulate the ecological resistance distribution in the high-latitude cold black soil region; collecting land use type data and topographic data in the region, introducing seasonal factors, and adjusting the spatial distribution of the resistance surface to construct a preliminary ecological resistance surface model; finally, using remote sensing images and GIS technology to couple and calculate the geographical detector and entropy weight method to improve the accuracy and reliability of the resistance surface.

[0014] Preferably, the method for extracting the ecological corridors includes: using the minimum cumulative resistance model and circuit theory to extract existing ecological corridors and potential ecological corridors; and evaluating the robustness of the optimized ecological network under different scenarios through network topology analysis methods.

[0015] Preferably, taking the center point of the ecological source area as the ecological source point, taking the identified ecological corridors and potential ecological corridors as edges and supplementary edges respectively, and evaluating the importance of the ecological source point by comprehensive importance:

[0016] CI i =αDC i +βCC i +γBC i

[0017] where CI i is the comprehensive importance of the node; DC i is the degree centrality; CC i is the closeness centrality; BC i is the betweenness centrality; α, β, and γ are weights, representing the contributions of degree centrality, closeness centrality, and betweenness centrality to the comprehensive importance respectively.

[0018] Preferably, comprehensively consider the average ecological risk value, total cost, and width coefficient of variation to calculate the risk value of each corridor. The formula includes:

[0019]

[0020] Among them, AR represents the average ecological risk value; TC represents the total cost; CW represents the coefficient of variation of width; AR i represents the average ecological risk value of the i-th corridor; L i is the length of the i-th corridor; RA i is the adjusted risk value of the i-th corridor; RP i is the risk penalty multiplier of the i-th corridor, and its value is determined according to the quantile of the corridor risk value. Specifically, if the average risk value of the corridor exceeds the 75th percentile of the overall average risk value, the penalty multiplier is set to 2; otherwise, it remains 1; σ(W) is the standard deviation of the corridor width; μ(W) is the mean value of the corridor width; N represents the total number of path points of the i-th ecological corridor.

[0021] Preferably, based on the risk value of each corridor, optimization is carried out:

[0022]

[0023] Among them, represents the corridor optimization index; W is the corridor width, and w1, w2, and w3 are weight coefficients.

[0024] The present invention also provides a construction system for the ecological security pattern in the high-latitude cold black soil area. The system is used to implement the above method and includes: an identification module, a construction module, an extraction module, and an optimization module;

[0025] The identification module is used to identify the comprehensive ecological source areas in the high-latitude cold black soil area based on the InVEST model and landscape morphological spatial pattern analysis;

[0026] The construction module is used to construct a multi-dimensional resistance surface based on the identified comprehensive ecological source areas;

[0027] The extraction module is used to extract ecological corridors based on the constructed resistance surface;

[0028] The optimization module is used to optimize the extracted ecological corridors to complete the construction of the ecological security pattern in the cold black soil area.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] (1) By using circuit theory to identify potential ecological corridors, taking into account ecological risks, construction costs, and the connectivity of the ecological network, it can maximize the stability and connectivity of the ecological security pattern in the cold black soil area while meeting ecological requirements, and ensure the adaptability of the ecological network under extreme climate and human activity disturbances;

[0031] (2) By adopting genetic algorithm for comprehensive optimization, while ensuring the diversity of the algorithm, it avoids local optimal solutions, greatly improves the global optimization ability, effectively improves the calculation efficiency, and ensures that the optimization results better meet the actual application requirements;

[0032] (3) Incorporating factors such as ecological risk, cost, and width variation coefficient into the comprehensive objective function, a general scenario ecological network optimization model with strong adaptability for cold region black soil areas is constructed, providing theoretical support for the planning and design of ecological security networks in cold region black soil areas, and providing a scientific basis and decision-making reference for decision-makers;

[0033] (4) By classifying the corridor lengths and assigning adaptive weights, it ensures the balance of optimization objectives for corridors of different lengths, effectively meets the optimization requirements under different environmental and ecological conditions, and provides comprehensive guarantee for the diversification and regional adaptability of ecological security networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0035] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention;

[0036] Figure 2 It is a schematic diagram of the comprehensive importance of ecological source points in the benchmark scenario (2020) of the typical high-latitude cold region black soil area basin according to an embodiment of the present invention;

[0037] Figure 3 It is a schematic diagram of the comprehensive importance of ecological source points in the low-carbon future scenario (SSP119 scenario, 2030) of the typical high-latitude cold region black soil area basin according to an embodiment of the present invention;

[0038] Figure 4 It is a schematic diagram of the comprehensive importance of ecological source points in the medium-carbon future scenario (SSP245 scenario, 2030) of the typical high-latitude cold region black soil area basin according to an embodiment of the present invention;

[0039] Figure 5 It is a schematic diagram of the comprehensive importance of ecological source points in the high-carbon future scenario (SSP545 scenario, 2030) of the typical high-latitude cold region black soil area basin according to an embodiment of the present invention;

[0040] Figure 6 It is a comparison diagram of the optimization before and after of ecological nodes in the benchmark scenario and future scenarios of the typical high-latitude cold region black soil area basin according to an embodiment of the present invention under two situations of random attack and arbitrary attack;

[0041] Figure 7 This is a comparison chart of the ecological corridor risks before and after optimization in the benchmark scenario and future scenario of a typical high-latitude cold-region black soil area basin in the embodiments of the present invention;

[0042] Figure 8 This is a comparison chart of the ecological corridor widths before and after optimization in the benchmark scenario and future scenario of a typical high-latitude cold-region black soil area basin in the embodiments of the present invention. Detailed implementation manners

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0044] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the drawings and specific implementation manners.

[0045] Embodiment 1

[0046] As can be seen from the background art, the prior art has the following defects:

[0047] (1) Lack of consideration of seasonal factors: The prior art did not fully consider the impact of seasonal factors (such as the number of snow-covered days, temperature changes, etc.) on the function of ecological corridors when designing the ecological security pattern, resulting in poor connectivity of ecological corridors under snow-covered conditions and being unable to effectively support the seasonal migration of species in the high-latitude cold-region black soil area.

[0048] (2) Ignoring natural resistance factors: The traditional ecological security model ignores the influence of natural resistance factors such as snow, ice, and frozen soil, and is unable to effectively support the migration and survival of species in the high-latitude cold-region black soil area, resulting in limited ecological network functions.

[0049] (3) Insufficient optimization of ecological corridor width: The prior art has limited research on the optimization of the width of ecological corridors. How to optimize the ecological security pattern in multiple scenarios while taking into account the coordinated optimization of ecological risks, the construction cost of ecological corridors, and the change intensity of the identified ecological corridor width is a key problem that the prior art has not been able to solve.

[0050] This embodiment provides a method for constructing an ecological security pattern in a high-latitude cold-region black soil area, and its flow block diagram is as Figure 1 shown, and the steps include:

[0051] S1. Identify the comprehensive ecological source areas in the high-latitude cold region with black soil based on the InVEST model and landscape morphological spatial pattern analysis.

[0052] Identify the functional ecological source areas (FES) and structural ecological source areas (SES) in the high-latitude cold region with black soil through the InVEST model and landscape morphological spatial pattern analysis (MSPA), and construct the comprehensive ecological source areas (IES) through spatial overlay.

[0053] Take the basin (Songhua River Basin) in the typical high-latitude cold region with black soil in China as an example.

[0054] (1) Collection of basin data in the high-latitude cold region with black soil. Collect the meteorological data of the basin in the high-latitude cold region with black soil, including precipitation and humidity, land use data, using SSP119 land use data as the low-carbon emission scenario, SSP245 land use data as the medium-carbon emission scenario, and SSP545 land use data as the high-carbon emission scenario, digital elevation model (DEM), net primary productivity of vegetation (NPP), soil profile data, road and river vector data, bedrock depth map, ecological geographical division data, and snow cover days data.

[0055] (2) Identification of functional ecological sources in the basin of the high-latitude cold region with black soil. Use the InVEST model to quantify the types and spatial distribution ranges of the main ecosystem services in the basin of the high-latitude cold region with black soil, calculate the relevant ecosystem service data, estimate the habitat quality (HQ), water yield (WY), and soil conservation (SC), use the net primary productivity (NPP) as a proxy indicator for carbon storage (CS), construct the comprehensive ecosystem service (CES) through normalization and spatial overlay, and determine the ecosystem service threshold. Calculate the CES of the future scenario according to the land use type data under different scenarios, and exclude the functional ecological source (FES) patches with an area of less than 10 km 2 2.

[0056] (3) Identification of structural ecological sources in the basin of the high-latitude cold region with black soil. Identify seven landscape categories, namely core area, isolated island, pore, edge area, roundabout area, bridging area, and branch line, through the MSPA model, and preferentially select the core areas with an area ≥ 10 km 2 2 as the structural ecological source (SES). Calculate the SES of the future scenario according to the land use type data under different scenarios. For those with an area less than 10 km 2SES patches were excluded. Compared with the baseline scenario (in 2020), in the low-carbon emission scenario (SSP119 - 2030), the proportion of the core area increased from 59.4% to 75.4%; however, the high-carbon emission scenario (SSP545 - 2030) led to the fragmentation of the core area. The areas and proportions of the annual MSPA analysis results for the baseline scenario (in 2020), low-carbon emission scenario (SSP119 - 2030), medium-carbon emission scenario (SSP245 - 2030), and high-carbon emission scenario (SSP545 - 2030) are shown in Table 1 as follows:

[0057] Table 1

[0058]

[0059] (4) The above-mentioned functional ecological sources (FES) and structural ecological source areas (SES) were merged through spatial overlay to construct an integrated ecological source area (IES) for subsequent ecological corridor planning. The ecological source areas and spatial distributions under different scenarios served as one of the basic data for establishing the ecological security network optimization model.

[0060] S2. Based on the identified integrated ecological source area, a multi-dimensional resistance surface was constructed.

[0061] Considering the special climatic characteristics of the high-latitude cold black soil region, especially factors such as snow cover, temperature, altitude, etc., the present invention constructed a multi-dimensional resistance surface. By combining human interference and natural constraint factors, the ecological resistance distribution in the high-latitude cold black soil region was accurately simulated, and then the layout of ecological corridors was optimized.

[0062] The resistance surface was constructed by integrating natural factors (such as terrain, vegetation type, water body distribution, etc.) and human factors (such as roads, cities, agricultural land, etc.). Each factor was assigned different weights to reflect their influence on species migration. In this embodiment, by collecting land use type data and terrain data (such as altitude, slope, etc.) in the region and introducing seasonal factors such as the number of snow cover days, the spatial distribution of the resistance surface was adjusted to cope with the unique climatic conditions in the high-latitude cold black soil region, ensuring the adaptability of the model to the high-latitude cold black soil region, and constructing a preliminary ecological resistance surface model.

[0063] The resistance surface distribution obtained by the above method was divided into 5 resistance levels, namely 1 - 5, which respectively represented the resistance degree from low to high, reflecting the resistance degree to species migration.

[0064] In addition, this embodiment also uses remote sensing images and GIS technology to perform coupled calculations of the geographical detector and the entropy weight method; the entropy weight method calculates the weight of each factor based on the distribution characteristics of the data, and the geographical detector analysis can identify the spatial relationships between various factors. According to the weights, the resistance surfaces at the above-mentioned levels 1-5 that have been divided are superimposed, as shown in Table 2, and the weights of the resistance factors in different scenarios are further adjusted to improve the accuracy and reliability of the resistance surface and ensure the comprehensive consideration of different factors in the model.

[0065] The steps of the above entropy weight method include:

[0066] (1) First, standardize the data

[0067] The normalization formula for positive indicators is:

[0068]

[0069] The normalization formula for negative indicators is:

[0070]

[0071] Among them, X gh represents the normalization result of the g-th object on the h-th indicator; x gh represents the original value of the g-th object on the h-th indicator.

[0072] (2) Calculate the weights of each indicator:

[0073]

[0074] Among them, ω h is the weight of the h-th indicator.

[0075] Table 2

[0076]

[0077] According to the above single-factor resistance surface and weights, a comprehensive resistance surface is constructed. The average resistance values of the resistance surfaces in the baseline scenario (2020), low-carbon emission scenario (SSP119-2030), medium-carbon emission scenario (SSP245-2030), and high-carbon emission scenario (SSP545-2030) are 3.13, 3.04, 3.14, and 3.14 respectively.

[0078] S3. Based on the constructed resistance surface, extract ecological corridors.

[0079] The minimum cumulative resistance model (MCR) and circuit theory are used to extract existing ecological corridors (EC) and potential ecological corridors (PEC); and the robustness of the optimized ecological network under different scenarios is evaluated through network topology analysis methods to ensure its long-term stability and ecological functions, and to improve ecological connectivity and the adaptability of the ecological network.

[0080] Specifically, the method for extracting ecological corridors includes:

[0081] According to the above-mentioned comprehensive resistance surface, the minimum cumulative resistance model (MCR) is used to extract ecological corridors (EC).

[0082] The formula for the minimum resistance model is:

[0083]

[0084] where f min is a positive correlation function reflecting the minimum cumulative resistance of a point and its distance to the source area and ecological processes; D cd is the spatial distance from ecological source c to ecological source d; R i is the resistance coefficient of a certain landscape unit in the region to the movement and diffusion in a certain direction, which is replaced by the above-mentioned resistance value in the present invention; ε, represents the total number of positions.

[0085] The numbers of ecological corridors in the baseline scenario (2020), low-carbon emission scenario (SSP119-2030), medium-carbon emission scenario (SSP245-2030), and high-carbon emission scenario (SSP545-2030) are 498, 471, 513, and 557 respectively. Due to the relatively high resistance values in key ecological areas under the high-carbon emission scenario, there are more phenomena of fragmentation and disconnection of ecological source areas. Potential ecological corridors refer to ecological channels that may exist under current conditions. Although these channels may not have been fully utilized or developed, in the present invention, circuit theory is used to regard ecological source areas as "nodes" in the circuit, and each node corresponds to a voltage source. If there are δ ecological source areas in the study area, then δ(δ-1) / 2 pairs of node combinations are formed. The Circuitscape software is used to simulate the random walk of electrons in the circuit, analogize the random drift behavior during species migration, calculate the current density distribution of all node pairs, and extract potential ecological corridors (PEC).

[0086] After that, according to the ecological corridors and potential ecological corridors extracted under different scenarios, network topology analysis methods are used to evaluate the connectivity and stability of the ecological network under different scenarios. In this embodiment, the center point of the ecological source area is used as the ecological source point (node), the identified ecological corridors and potential ecological corridors are used as edges and supplementary edges respectively, and the comprehensive importance (CI) is used to evaluate the importance of the ecological source point (node). The calculation method of CI is:

[0087] CI i = αDC i + βCC i + γBC i

[0088] Wherein, CI i is the comprehensive importance of the node; DC i is the degree centrality; CC i is the closeness centrality; BC i is the betweenness centrality; α, β, and γ are weights, representing the contributions of degree centrality, closeness centrality, and betweenness centrality to the comprehensive importance, respectively.

[0089] After the above weights α, β, and γ are normalized by each index of the node, the minimum redundancy maximum relevance (mRMR) method is used to calculate the weights, and its objective function is as follows:

[0090]

[0091] ω′ g is the mRMR weight of the gth index; corr(x g , x h ) is the correlation coefficient between the gth index and the hth index.

[0092] The calculation results of the comprehensive importance, degree centrality, closeness centrality, and betweenness centrality of the ecological corridor and the optimized ecological corridor under the baseline scenario (2020), low-carbon emission scenario (SSP119-2030), medium-carbon emission scenario (SSP245-2030), and high-carbon emission scenario (SSP545-2030) are shown in Table 3. The spatial distribution of the comprehensive importance of the ecological source points under the baseline scenario (2020), low-carbon emission scenario (SSP119-2030), medium-carbon emission scenario (SSP245-2030), and high-carbon emission scenario (SSP545-2030) is as Figures 2 - 5 shown.

[0093] Table 3

[0094]

[0095] After the ecological corridor is extracted, the robustness of the regional ecological network (i.e., the ability of the system to maintain performance under uncertain conditions) is evaluated. The steps include: realizing by evaluating the comprehensive importance and robustness of the nodes in the two modes of EC and EC+PEC, and calculating the robustness in two modes of random attack (randomly attacking nodes) and deliberate attack (preferentially attacking nodes with high comprehensive importance values).

[0096] Such as Figure 6As shown in the figure, before optimization, the ecological network was very vulnerable under targeted attacks. When the attack rate exceeded 40%-50%, the connectivity decreased sharply; under random attacks, the connectivity decreased gradually. After optimization, the robustness was significantly improved. Especially in the SSP245-2030 scenario, when the attack rate exceeded 60%, the network connectivity remained high. In the 2020 scenario, the targeted attack had a greater impact on the network, but the robustness increased after optimization, the random attack had a smaller impact, and the network recovery ability was enhanced. In the low-carbon emission scenario (SSP119-2030), the stability of the optimized network increased, but it still decreased significantly after the attack rate exceeded 40%-50%; in the medium-carbon emission scenario (SSP245-2030), when the attack rate exceeded 60% after optimization, the network connectivity remained high and the robustness increased significantly; while in the high-carbon emission scenario (SSP545-2030), the optimization effect was weak, and the network connectivity decreased significantly after the attack rate exceeded 40%-50%. Generally speaking, optimizing the ecological corridor significantly enhances the robustness of the ecological network in the medium-carbon emission scenario, and the optimization effect is limited in extreme scenarios (high-carbon or low-carbon).

[0097] S4. Optimize the extracted ecological corridors to complete the construction of the ecological security pattern in the cold region black soil area.

[0098] Through the combination of ecological risk and genetic algorithm, optimize the corridor width to maximize the robustness and connectivity of the ecological network on the premise of controlling ecological risk and construction cost, and obtain the construction plan for the ecological security pattern in the cold region black soil area.

[0099] The ecological risk assessment model considers various environmental factors, such as climate change, land use, species threat, etc., and calculates the ecological risk of each ecological corridor. The basic idea of this model is to quantify the ecological risk at different widths by evaluating the potential threats and vulnerabilities in the ecosystem. The ecological risk function can be expressed as:

[0100]

[0101] Among them, p is the landscape type, and the larger the value, the higher the ecological risk; A kp is the area of landscape type p in the kth risk zone; A k is the total area of the kth risk zone; N is the total number of landscape types; D p is the landscape disturbance index; V p is the landscape vulnerability index.

[0102] According to existing research and the ecological characteristics of the study area, in this embodiment, the normalized vulnerability indices of cultivated land, grassland, shrubbery, forest land, construction land, water body and bare land are taken as 0.179, 0.143, 0.107, 0.071, 0.036, 0.214 and 0.250 respectively.

[0103] After that, the risk value (RV) of the ecological corridor is allocated. By spatially overlaying the vector coordinate points of the corridor with the risk raster, the risk values of all points on each corridor are extracted. The average risk value (AR) is calculated using the following formula:

[0104]

[0105] where i is the corridor number; N is the total number of path points of the i-th ecological corridor; RV ij is the ecological risk value of the j-th grid point within the i-th corridor.

[0106] To achieve the multi-objective balanced optimization of corridors with different lengths, in this embodiment, according to the corridor length (L), it is divided into 4 length classes (LC), including: short, medium, long, and extra-long corridors. The calculation formula is:

[0107]

[0108] According to the above formula, differential fitness weights (w a ) are assigned to corridors of each class. The calculation formula is:

[0109]

[0110] According to the above formula, a calculation formula that comprehensively considers the average ecological risk value (AR), the total cost (TC), and the coefficient of variation of width (CW) is constructed. The specific formula is expressed as follows:

[0111]

[0112] where L i is the length of the i-th corridor; RA i is the adjusted risk value of the i-th corridor; RP i is the risk penalty multiplier of the i-th corridor, and its value is determined according to the quantile of the corridor risk value. Specifically, if the average risk value of the corridor exceeds the 75th percentile of the overall average risk value, the penalty multiplier is set to 2; otherwise, it remains 1; σ(W) is the standard deviation of the corridor width; μ(W) is the mean value of the corridor width.

[0113] Finally, the optimization objective function is defined. First, the optimization objective function is set, comprehensively considering ecological risk, corridor construction cost, and ecological network stability. The objective function is:

[0114]

[0115] where Indicates the corridor optimization index; W is the corridor width, and w1, w2, and w3 are weight coefficients. In this embodiment, they are taken as 0.7, 0.2, and 0.1 respectively. The objective function is optimized by the genetic algorithm to balance the three objective values, and finally the optimal corridor width is obtained.

[0116] As Figure 7 shown, in this embodiment, the average risk of short corridors in the benchmark scenario (2020) of the typical high-latitude cold black soil area basin is 0.0276. After optimization, it hardly changes and remains at 0.0276. The risk values in other emission scenarios (SSP1192030, SSP2452030, SSP5452030) are 0.0309, 0.0318, and 0.0321 respectively. After optimization, they also remain unchanged or change very little, indicating that the optimization measures have little impact on the risk of short corridors.

[0117] The risk value of the medium corridor shows significant changes. The risk in 2020 is 0.0321, and it drops to 0.0311 after optimization. In the SSP1192030 and SSP2452030 scenarios, the risk drops from 0.0330 to 0.0328 and from 0.0337 to 0.0332 respectively after optimization. In the SSP5452030 scenario, the risk drops from 0.0335 to 0.0332, indicating that the optimization measures effectively reduce the ecological risk of the medium corridor.

[0118] The ecological risk of the long corridor also decreases to a certain extent after optimization. The risk in 2020 is 0.0363, and it drops to 0.0346 after optimization. The risk in the SSP1192030 scenario drops from 0.0355 to 0.0352, the risk in the SSP2452030 scenario drops from 0.0352 to 0.0344, and the risk in the SSP5452030 scenario drops from 0.0355 to 0.0346, indicating that the optimization measures have obvious effects in reducing the ecological risk of the long corridor.

[0119] The risk of the ultra-long corridor is 0.0364 in 2020 and drops to 0.0354 after optimization. In the SSP1192030 and SSP2452030 scenarios, the optimized risks drop from 0.0379 to 0.0376 and from 0.0376 to 0.0368 respectively. In the SSP5452030 scenario, the risk drops from 0.0375 to 0.0375, indicating that the optimized scenario can effectively alleviate the ecological risk of the ultra-long corridor.

[0120] The optimized scenarios generally and effectively reduce the ecological risks of different corridor types. Especially in the medium corridor and the long corridor, the optimization measures show significant risk reduction effects.

[0121] As Figure 8As shown, in this embodiment, the width of the short corridor in the benchmark scenario (2020) of the typical high-latitude cold black soil area watershed is 696.79 meters, which is reduced to 632.23 meters after optimization. Under the SSP1192030 scenario in 2030, the width of the short corridor is 719.75 meters, and after optimization it is 627.26 meters; under the SSP2452030 scenario in 2030, the width is 680 meters, and after optimization it is 619.97 meters; under the SSP5452030 scenario in 2030, the width is 648.31 meters, and after optimization it is 633.42 meters;

[0122] The width of the medium corridor in 2020 is 739.61 meters, which is reduced to 674.79 meters after optimization. Under the SSP1192030 scenario in 2030, the width is 761.03 meters, and after optimization it is 677.48 meters; under the SSP2452030 scenario in 2030, the width is 717.15 meters, and after optimization it is 657.15 meters; under the SSP5452030 scenario in 2030, the width is 688.00 meters, and after optimization it is 671.89 meters;

[0123] The width of the long corridor in 2020 is 706.62 meters, which is reduced to 630.91 meters after optimization. Under the SSP1192030 scenario in 2030, the width is 718.83 meters, and after optimization it is 640.17 meters; under the SSP2452030 scenario in 2030, the width is 677.64 meters, and after optimization it is 615.99 meters; under the SSP5452030 scenario in 2030, the width is 660.48 meters, and after optimization it is 631.01 meters;

[0124] The width of the extra-long corridor in 2020 is 586.36 meters, which is reduced to 537.8 meters after optimization. Under the SSP1192030 scenario in 2030, the width is 696.90 meters, and after optimization it is 635.49 meters; under the SSP2452030 scenario in 2030, the width is 618.58 meters, and after optimization it is 574.36 meters; under the SSP5452030 scenario in 2030, the width is 626.86 meters, and after optimization it is 594.13 meters.

[0125] Furthermore, the present invention can also be extended to the construction of ecological security patterns in different climate zones and geographical regions. For example, in addition to the high-latitude cold black soil area, the method in the invention can also be applied to the optimization of ecological corridors in temperate and subtropical regions. By adjusting the weights of different factors in the resistance surface and simulating different climate conditions, it is possible to flexibly respond to the challenges of various ecological environments and provide ecological security planning solutions for different regions and climate scenarios. In addition, the present invention can also be combined with other ecological protection measures, such as wetland protection and forest restoration, to further enhance the connectivity of ecological corridors and the living space of species, thereby providing multi-dimensional solutions for global ecological security.

[0126] Embodiment 2

[0127] This embodiment also provides a construction system for the ecological security pattern in the high-latitude cold black soil area, including: an identification module, a construction module, an extraction module, and an optimization module; the identification module is used to identify the comprehensive ecological source areas in the high-latitude cold black soil area based on the InVEST model and landscape morphological spatial pattern analysis; the construction module is used to construct a multi-dimensional resistance surface based on the identified comprehensive ecological source areas; the extraction module is used to extract ecological corridors based on the constructed resistance surface; the optimization module is used to optimize the extracted ecological corridors to complete the construction of the ecological security pattern in the cold black soil area.

[0128] Next, this embodiment will be combined to detail how the present invention solves technical problems in real life.

[0129] First, the acquisition module identifies the functional ecological source areas (FES) and structural ecological source areas (SES) in the high-latitude cold black soil area through the InVEST model and landscape morphological spatial pattern analysis (MSPA), and constructs the comprehensive ecological source areas (IES) through spatial superposition.

[0130] Its specific process includes:

[0131] (1) Regional data collection: Collect meteorological data in the region, including precipitation, humidity, land use data, land use data under low-carbon emission scenarios, medium-carbon emission scenarios, and high-carbon emission scenarios, digital elevation model (DEM), vegetation net primary productivity (NPP), soil profile data, road and river vector data, bedrock depth map, ecological geographical division data, and snow cover days data.

[0132] (2) Identification of functional ecological sources. Use the InVEST model to quantify the types and spatial distribution ranges of the main ecosystem services in the region, calculate relevant ecosystem service data, estimate the habitat quality (HQ), water yield (WY), and soil conservation (SC), use the net primary productivity (NPP) as a proxy indicator for carbon storage (CS), construct the comprehensive ecosystem service (CES) through normalization and spatial superposition, and determine the ecosystem service threshold. Calculate the CES in the future scenario according to the land use type data under different scenarios, and exclude the functional ecological source (FES) patches with an area less than 10 km 2 (3) Identification of structural ecological sources. Identify seven landscape categories, namely core area, islet, pore, edge area, roundabout area, bridging area, and branch line, through the MSPA model, and preferentially select the core areas with an area ≥ 10 km

[0133] as the structural ecological source areas (SES). Calculate the SES in the future scenario according to the land use type data under different scenarios. Exclude the SES patches with an area less than 10 km 2 (24) For the SES patches smaller than 10 km 2 are excluded.

[0134] (4) The above functional ecological sources (FES) and structural ecological source areas (SES) are merged through spatial overlay to construct an integrated ecological source area (IES) for subsequent ecological corridor planning. The ecological source areas and spatial distributions under different scenarios serve as one of the basic data for establishing the ecological security network optimization model.

[0135] After that, considering the special climate characteristics of the high-latitude cold black soil region, especially factors such as snow cover, temperature, altitude, etc., the present invention constructs a multi-dimensional resistance surface. By combining human disturbance and natural constraint factors, the ecological resistance distribution in the high-latitude cold black soil region is accurately simulated, and then the layout of the ecological corridor is optimized. The module constructs a multi-dimensional resistance surface based on the identified integrated ecological source area.

[0136] The specific process includes:

[0137] (1) The resistance surface is constructed by integrating natural factors (such as terrain, vegetation type, water body distribution, etc.) and human factors (such as roads, cities, agricultural land, etc.). Each factor is given different weights to reflect their influence on species migration;

[0138] (2) Collect land use type data and terrain data (such as altitude, slope, etc.) in the region, introduce seasonal factors such as the number of snow cover days, and adjust the spatial distribution of the resistance surface to cope with the unique climate conditions in the high-latitude cold black soil region, ensuring the adaptability of the model to the high-latitude cold black soil region, and constructing a preliminary ecological resistance surface model;

[0139] (3) The resistance surface distribution obtained by the above method is divided into 5 resistance levels, namely 1-5, which respectively represent the resistance degree from low to high, reflecting the resistance degree to species migration;

[0140] (4) Using remote sensing images and GIS technology, an innovative combination of the geographical detector-entropy weight method coupling calculation method is used. The entropy weight method calculates the weight of each factor based on the distribution characteristics of the data, and the geographical detector analysis can identify the spatial relationship between various factors. According to the weights, the above-mentioned resistance surfaces divided into 1-5 levels are overlaid, and the weights of the resistance factors under different scenarios are further adjusted to improve the accuracy and reliability of the resistance surface, ensuring the comprehensive consideration of different factors by the model.

[0141] The extraction module extracts ecological corridors based on the constructed resistance surface.

[0142] Existing ecological corridors (EC) and potential ecological corridors (PEC) are extracted through the minimum cumulative resistance model (MCR) and circuit theory. The robustness of the optimized ecological network under different scenarios is evaluated through network topology analysis methods to ensure its long-term stability and ecological functions, and to enhance ecological connectivity and the adaptability of the ecological network.

[0143] The specific process includes:

[0144] (1) The present invention extracts ecological corridors (EC) based on the Minimum Cumulative Resistance model (MCR);

[0145] (2) Potential ecological corridors refer to ecological passages that may exist under current conditions. Although these passages may not have been fully utilized or developed, the present invention uses Circuit Theory to simulate the random drift behavior during species migration and extracts potential ecological corridors (PEC);

[0146] (3) According to the ecological corridors and potential ecological corridors extracted under different scenarios, the network topology analysis method is used to evaluate the connectivity and stability of the ecological network under different scenarios. The present invention takes the center point of the ecological source area as the ecological source point (node), takes the identified ecological corridors and potential ecological corridors as edges and supplementary edges respectively, and uses the Comprehensive Importance (CI) to evaluate the importance of the ecological source point (node). The calculation method of CI is:

[0147] CI i = αDC i + βCC i + γBC i

[0148] Wherein, CI i is the comprehensive importance of the node, DC i is the degree centrality, CC i is the closeness centrality, BC i is the betweenness centrality. Among them, α, β, and γ are weights, representing the contributions of degree centrality, closeness centrality, and betweenness centrality to the comprehensive importance respectively.

[0149] (4) The above weights α, β, and γ are respectively calculated by the minimum redundancy maximum relevance (mRMR) method after normalizing each index of the node;

[0150] (5) The robustness of the regional ecological network (i.e., the ability of the system to maintain performance under uncertain conditions) is realized by evaluating the comprehensive importance and robustness of the nodes in the two modes of EC and EC+PEC, and two modes of random attack (randomly attacking nodes) and deliberate attack (preferentially attacking nodes with high comprehensive importance values) are used to calculate the robustness.

[0151] Finally, the optimization module is used to optimize the extracted ecological corridors to complete the construction of the ecological security pattern in the cold black soil area.

[0152] By combining ecological risk and genetic algorithm, optimize the corridor width to ensure the maximization of the robustness and connectivity of the ecological network while controlling ecological risks and construction costs, and obtain the construction plan for the ecological security pattern in the cold black soil region.

[0153] Its specific process includes:

[0154] (1) The ecological risk assessment model considers various environmental factors, such as climate change, land use, species threat, etc., and calculates the ecological risk of each ecological corridor. The basic idea of this model is to quantify the ecological risk at different widths by evaluating the potential threats and vulnerabilities in the ecosystem. The ecological risk function can be expressed as:

[0155]

[0156] where p is the landscape type, and the larger the value, the higher the ecological risk; A kp is the area of landscape type p in the kth risk zone; A k is the total area of the kth risk zone; N is the total number of landscape types; D p is the landscape disturbance index; V p is the landscape vulnerability index.

[0157] According to existing research and the ecological characteristics of the study area, the normalized vulnerability indices of cultivated land, grassland, shrub forest, forest land, construction land, water body and bare land are 0.179, 0.143, 0.107, 0.071, 0.036, 0.214 and 0.250 respectively.

[0158] (2) Allocate the ecological corridor risk value (RV). Through spatial overlay, overlay the vector coordinate points of the corridor with the risk raster to extract the risk values of all points on each corridor. Calculate the average risk value (AR) through the following formula:

[0159]

[0160] where i is the corridor number; N is the total number of path points of the ith ecological corridor; RV ij is the ecological risk value of the jth grid point in the ith corridor.

[0161] (3) To achieve the multi-objective balanced optimization of corridors with different lengths, according to the corridor length (L), this invention divides it into 4 length levels (LC), short, medium, long and extra-long corridors, and the calculation formula is:

[0162]

[0163] (4) According to the above formula, assign different fitness weights (w a ) to corridors of each level, and the calculation formula is:

[0164]

[0165] (5) According to the above formula, a calculation formula that comprehensively considers the average ecological risk value (RV), total cost (TC), and coefficient of variation of width (CW) is constructed, and the specific formula is expressed as follows:

[0166]

[0167]

[0168] L i is the length of the i-th corridor; RA i is the adjusted risk value of the i-th corridor; RP i is the risk penalty multiplier of the i-th corridor, and its value is determined according to the quantile of the corridor risk value. Specifically, if the average risk value of the corridor exceeds the 75th percentile of the overall average risk value, the penalty multiplier is set to 2; otherwise, it remains 1; σ(W) is the standard deviation of the corridor width; μ(W) is the mean value of the corridor width.

[0169] (6) Further define the optimization objective function. First, set the optimization objective function, comprehensively considering ecological risk, corridor construction cost, and ecological network stability. The objective function is:

[0170]

[0171] Among them, represents the corridor optimization index; W is the corridor width, and w1, w2, w3 are weight coefficients, which are 0.7, 0.2, and 0.1 respectively. Optimize this objective function through the genetic algorithm to balance the three objective values and finally obtain the optimal corridor width.

[0172] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for constructing an ecological security pattern in the high-latitude cold black soil area, characterized in that the steps Including: Based on the InVEST model and landscape morphological spatial pattern analysis, identify the comprehensive ecological source areas in the high-latitude cold region with black soil; Based on the identified comprehensive ecological source areas, construct a multi-dimensional resistance surface; Based on the constructed resistance surface, extract ecological corridors; Optimize the extracted ecological corridors to complete the construction of the ecological security pattern in the cold region with black soil.

2. The construction method of the ecological security pattern in the high-latitude cold black soil area according to claim 1, characterized in that, The method for identifying the comprehensive ecological source areas includes: identifying the functional ecological source areas and structural ecological source areas in the high-latitude cold region with black soil through the InVEST model and landscape morphological spatial pattern analysis, and constructing the comprehensive ecological source areas through spatial overlay.

3. The method for constructing the ecological security pattern in the high-latitude cold black soil region according to claim 1, wherein The method for constructing the multi-dimensional resistance surface includes: combining human interference and natural constraint factors to simulate the ecological resistance distribution in the high-latitude cold region with black soil; collecting land use type data and topographic data in the region, introducing seasonal factors, and adjusting the spatial distribution of the resistance surface to construct a preliminary ecological resistance surface model; finally, using remote sensing images and GIS technology to couple and calculate the geographical detector and the entropy weight method.

4. The method for constructing the ecological security pattern in the high-latitude cold black soil area according to claim 1, characterized in that, The method for extracting the ecological corridors includes: using the minimum cumulative resistance model and circuit theory to extract existing ecological corridors and potential ecological corridors; and evaluating the robustness of the optimized ecological network under different scenarios through network topology analysis methods.

5. The method for constructing the ecological security pattern in the high-latitude cold black soil area according to claim 4, characterized in that Taking the center points of the ecological source areas as ecological source points, taking the identified ecological corridors and potential ecological corridors as edges and supplementary edges respectively, and evaluating the importance of the ecological source points using comprehensive importance: CI i = αDC i + βCC i + γBC i Among them, CI i is the comprehensive importance of the node; DC i is the degree centrality; CC i is the closeness centrality; BC i is the betweenness centrality; α, β, and γ are weights, representing the contributions of degree centrality, closeness centrality, and betweenness centrality to the comprehensive importance, respectively.

6. The method for constructing the ecological security pattern in the high-latitude cold black soil area according to claim 1, characterized in that Comprehensively considering the average ecological risk value, total cost and width variation coefficient to calculate the risk value of each corridor, and the formula includes: Among them, AR represents the average ecological risk value; TC represents the total cost; CW represents the coefficient of variation of width; AR i represents the average ecological risk value of the i-th corridor; L i is the length of the i-th corridor; RA i is the adjusted risk value of the i-th corridor; RP i is the risk penalty multiplier of the i-th corridor; σ(W) is the standard deviation of the corridor width; μ(W) is the mean value of the corridor width; N represents the total number of path points of the i-th ecological corridor.

7. The method for constructing the ecological security pattern in the high-latitude cold black soil region according to claim 6, wherein Based on the risk value of each corridor, conduct optimization: Among them, represents the corridor optimization index; W is the corridor width, and w1, w2, w3 are weight coefficients.

8. A construction system for the ecological security pattern in the high-latitude cold black soil area, the system is used to implement the method described in any one of claims 1-7, and is characterized in that, Including: An identification module, a construction module, an extraction module and an optimization module; The identification module is used to identify the comprehensive ecological source areas in the high-latitude cold region with black soil based on the InVEST model and landscape morphological spatial pattern analysis; The construction module is used to construct a multi-dimensional resistance surface based on the identified comprehensive ecological source areas; The extraction module is used to extract ecological corridors based on the constructed resistance surface; The optimization module is used to optimize the extracted ecological corridors to complete the construction of the ecological security pattern in the cold region with black soil.

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