Method for identifying ecological core area based on two-way feedback between land use and ecological network

Abstract

The application discloses a kind of ecological core area identification methods based on land use and ecological network two-way feedback, method includes obtaining land use data and multi-source driving factor data and pre-processing;According to the land use data and multi-source driving factor data after pre-processing, the development probability of land use type is calculated, and PLUS model is used to predict future land use pattern;Based on future land use pattern, ecological source is calculated, and the minimum resistance model is used to extract ecological corridor, according to ecological source and ecological corridor, ecological network is formed;The ecological network formed is fed back, the feedback process is iterated, when meeting stopping condition, iteration is stopped, the region of all ecological network superposition in iteration process is output, and the ecological core area is obtained.The application quantifies the dynamic mutual feedback mechanism of land use simulation and ecological network by combining them, breaks through the one-way analysis limitation of the relationship between land use and ecological network in traditional research, and improves the accuracy of ecological core area identification.

Description

Technical Field

[0001] This invention relates to the field of deep integration of geographic information systems (GIS) and ecology, and in particular to a method for identifying ecological core areas based on bidirectional feedback between land use and ecological networks. Background Technology

[0002] The rapid pace of urbanization has led to rapid socio-economic development, but it has also exacerbated the conflict between land use and ecological protection. The continuous expansion of construction land results in the reduction of biological habitats and landscape fragmentation, hindering the material cycle, energy flow, and information transmission of ecosystems, and threatening the stability of their ecological networks. Ecological networks are a key means of coordinating regional development and ecological protection. They are interconnected network structures composed of ecologically valuable source areas and ecological corridors connecting these source areas. These networks can connect fragmented patches, promote inter-patch species exchange and information transmission, and enhance ecosystem stability.

[0003] Current technologies mostly focus on the one-way impact between ecological networks and land use, either only focusing on the impact of land use change on the structure of ecological networks, or only analyzing the constraining effect of ecological network protection on land use change. They lack methods to explore the dynamic feedback mechanism between land use change and ecological network evolution, and also lack accurate identification of ecological core areas. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for identifying ecological core areas based on bidirectional feedback between land use and ecological networks, in order to explore the balance between land use and ecological networks and identify ecological core areas under this balance.

[0005] Therefore, the technical solution adopted by the present invention is as follows:

[0006] This invention provides a method for identifying ecological core areas based on bidirectional feedback between land use and ecological networks, the method comprising:

[0007] Acquire land use data and multi-source driving factor data, and perform preprocessing;

[0008] The development probability of land use types is calculated based on the preprocessed land use data and multi-source driving factor data, and the future land use pattern is predicted using the PLUS model.

[0009] Ecological source areas are calculated based on future land use patterns, and ecological corridors are extracted using a minimum resistance model. An ecological network is then formed based on the ecological source areas and ecological corridors.

[0010] Feedback is provided to the ecological network. Specifically, spatial distance analysis is performed on the ecological network to generate a feedback layer. Based on the feedback layer, the land use type development probability data input to the PLUS model is corrected, and the land use pattern is re-predicted. Based on the re-predicted land use pattern, a new ecological network is formed, and a new feedback layer is generated for a new round of feedback. The feedback process is iterated. When the stopping condition is met, the iteration stops, and the area where all ecological networks are superimposed during the iteration process is output to obtain the ecological core area.

[0011] According to the above scheme, multi-source driving factors specifically include social multi-source driving factors and natural multi-source driving factors; preprocessing specifically includes:

[0012] Land use data is reclassified, social multi-source driving factor data is processed using Euclidean distance, and all multi-source driving factor data is projected onto a projection coordinate system consistent with that of land use data. Resampling and masking are then performed to ensure that the resolution and spatial range of all data are consistent.

[0013] According to the above plan, predicting future land use patterns using the PLUS model specifically includes:

[0014] The processed land use data is converted into a model input format and then input into the model. Based on the PLUS model's land expansion extraction module, land use expansion data for the base period and the end period are obtained.

[0015] Based on the base and end-stage land use expansion data and the processed multi-source driving factor data, the development probability of land use types is obtained.

[0016] The land use type development probability is input into the CARS module in the PLUS model to obtain the predicted historical land use layout, and then compared with the actual historical land use layout to verify the accuracy.

[0017] To obtain future land use demand, the probability of land use type development and future land use demand are input into the CARS module of the PLUS model after accuracy verification, and the predicted future land use pattern is obtained.

[0018] According to the above scheme, accuracy verification specifically includes:

[0019] Based on the development probability of each land use type, the final land use data, and the land use quantity demand in historical years, the simulated and predicted historical land use layout is obtained through the CA simulation part based on multiple types of random patch seeds; the land use quantity demand in historical years is obtained from the land use data.

[0020] Accuracy is verified by comparing simulated land use predictions with actual land use data and using the kappa coefficient. The kappa coefficient is calculated based on the confusion matrix and numerical excellence function in the verification module of the PLUS model. When the kappa coefficient is higher than a certain threshold, the accuracy verification is considered successful.

[0021] According to the above plan, the ecological network composed of ecological source areas and ecological corridors specifically includes:

[0022] Landscape pattern analysis is conducted on future land use patterns to extract and screen potential ecological source areas, thus obtaining ecological source areas;

[0023] By selecting a resistance factor, the comprehensive resistance surface is calculated using the weighted linear combination method.

[0024] Based on the ecological source area and comprehensive resistance surface, ecological corridors are extracted and combined with the ecological source area to form an ecological network.

[0025] According to the above scheme, potential ecological source areas are extracted and screened, specifically based on the potential connectivity index and patch importance index of each potential ecological source area.

[0026] According to the above plan, the re-prediction of land use patterns specifically includes:

[0027] Euclidean distance analysis was performed on the ecological source area and the ecological corridor respectively. The two Euclidean distances were superimposed and then superimposed with the previous generation feedback layer to form a new generation feedback layer.

[0028] The probability of land use type development is corrected based on the feedback layer, and the corrected probability of land use type development is re-input into the PLUS model to predict the future land use pattern.

[0029] According to the above scheme, the ecological network specifically consists of ecological source areas and ecological corridors; the stopping condition specifically refers to the following: the ratio of the intersection area between the ecological source area in the current iteration and the ecological source area in the previous iteration, and the ratio of the intersection area between the ecological corridor in the current iteration and the ecological corridor in the previous iteration both exceed a certain threshold; or the ratio of the intersection area does not exceed a certain threshold, but the rate of change of the intersection area is less than the change threshold.

[0030] This invention also provides an ecological core area identification system based on bidirectional feedback between land use and ecological networks, the system comprising:

[0031] The data processing module is used to acquire land use data and multi-source driving factor data, and to perform preprocessing.

[0032] The land use pattern prediction module is used to calculate the development probability of land use types based on preprocessed land use data and multi-source driving factor data, and to predict future land use patterns using the PLUS model.

[0033] The ecological network calculation module is used to calculate ecological source areas based on future land use patterns and extract ecological corridors using a minimum resistance model, and to form an ecological network based on ecological source areas and ecological corridors.

[0034] The identification module is used to provide feedback to the ecological network. Specifically, it performs spatial distance analysis on the ecological network to generate a feedback layer, corrects the land use development probability data of the input PLUS model based on the feedback layer, and then re-predicts the land use pattern. Based on the re-predicted land use pattern, a new ecological network is formed, and a new feedback layer is generated for a new round of feedback. The feedback process is iterated. When the stopping condition is met, the iteration stops, and the area where all ecological networks are superimposed during the iteration process is output to obtain the ecological core area.

[0035] The present invention also provides a computer storage medium storing a computer program that can be executed by a processor, the computer program executing the above-described method for identifying ecological core areas based on bidirectional feedback of land use and ecological networks.

[0036] The beneficial effects of this invention are as follows: By combining land use simulation and ecological network, this invention deeply integrates the dynamic simulation capability of land use patterns of the PLUS model with the ecological network construction technology, quantifies the dynamic feedback mechanism between the two, breaks through the limitation of unidirectional analysis of the relationship between land use and ecological network in traditional research, and effectively improves the accuracy of ecological core area identification; and through multiple iterations, it visualizes the dynamic mutual influence effect between the two, extracts the ecological core area in the final state, and enhances the credibility of the obtained ecological core area.

[0037] Furthermore, the preprocessing of land use data and multi-source driving factor data in this invention projects both types of data onto a unified projection coordinate system, making the resolution and spatial range of all data consistent, which facilitates input into the model for calculation and improves the model's calculation efficiency.

[0038] Furthermore, this invention ensures the accuracy of the predicted future land use pattern by first verifying the accuracy of the CARS module of the PLUS model before predicting the future land use pattern. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the process flow of an ecological core area identification method based on two-way feedback between land use and ecological network according to an embodiment of the present invention.

[0040] Figure 2 This is a schematic diagram of the predicted distribution of ecological sources, resistance surfaces, and ecological corridors in a certain region in 2030 according to an embodiment of the present invention.

[0041] Figure 3 This is a schematic diagram illustrating the feedback layer correction of land use type development probability in an embodiment of the present invention;

[0042] Figure 4 This is a graph showing the trend of the area of ​​intersection and union of ecological source areas and ecological corridors during the iterative process of this invention embodiment;

[0043] Figure 5 This is the final identified distribution map of the ecological core area of ​​a certain region in this embodiment of the invention;

[0044] Figure 6 This is a schematic diagram of the system structure of the ecological core area identification system based on bidirectional feedback of land use and ecological network according to an embodiment of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0046] To explore the balance between land use and ecological networks and identify the ecological core area under this state, embodiments of the present invention provide a method for identifying the ecological core area based on bidirectional feedback between land use and ecological networks, such as... Figure 1 As shown, the method includes:

[0047] S1. Acquire land use data and multi-source driving factor data, and perform preprocessing.

[0048] S2. Calculate the development probability of land use types based on the preprocessed land use data and multi-source driving factor data, and use the PLUS model to predict future land use patterns.

[0049] S3. Calculate the ecological source areas based on the future land use pattern, and extract the ecological corridors using the minimum resistance model. Then, form an ecological network based on the ecological source areas and ecological corridors.

[0050] S4. Feedback is provided to the formed ecological network. Specifically, spatial distance analysis is performed on the ecological network to generate a feedback layer. Based on the feedback layer, the land use type development probability data input to the PLUS model is corrected, and the land use pattern is re-predicted. A new ecological network is formed based on the re-predicted land use pattern, and a new feedback layer is generated for a new round of feedback. The feedback process is iterated. When a stopping condition is met, the iteration stops, and the area where all ecological networks are superimposed during the iteration is output to obtain the ecological core area. The ecological network specifically includes ecological source areas and ecological corridors.

[0051] Specifically, this embodiment collects land use datasets for various periods and uses socio-economic data such as population, GDP, and transportation as social multi-source driving factors, and natural spatial data such as topography and climate as natural multi-source driving factors.

[0052] Specifically, the preprocessing includes: reclassifying land use data, performing Euclidean distance processing on traffic data of multi-source driving factors, projecting all multi-source driving factor data onto a projection coordinate system consistent with that of land use data, and using "resampling" and "mask processing" to ensure that the resolution and spatial range of all data are consistent.

[0053] In this embodiment, land use data (2010, 2015 and 2020) were collected and imported into ArcGIS software. Tools in the GIS software toolbox were used to ensure that the data format, row and column numbers, and coordinate projection were consistent. The land use data was then classified into six categories: cultivated land, forest land, grassland, water area, construction land and unused land using a reclassification tool.

[0054] Specifically, using the PLUS model to predict future land use patterns includes:

[0055] The processed land use data is converted into the model input format and then input into the model. Based on the PLUS model's land expansion extraction module, the base period and end period land use expansion data are obtained. In this embodiment, the PLUS model's format conversion tool is used to convert the processed land use data into an unsigned character format.

[0056] Based on the base and end-stage land use expansion data and the processed multi-source driving factor data, the development probability of land use types is obtained.

[0057] The land use type development probability is input into the CARS module in the PLUS model to obtain the predicted historical land use layout, and then compared with the actual historical land use layout to verify the accuracy.

[0058] To obtain future land use demand, the probability of land use type development and future land use demand are input into the CARS module of the PLUS model after accuracy verification, and the predicted future land use pattern is obtained.

[0059] The specific formula for the development probability of land use type is as follows:

[0060]

[0061] In the formula: The value can be 0 or 1. This indicates that other land use types are directed towards Land use change, Then it will not change. A vector composed of driving factors; In decision tree The land use prediction type calculated in real time; For the indicator function of the decision tree; for or Under the condition, mesh Place The probability of land use expansion; M is the total count of the decision tree. Using the LEAS module in the PLUS model, input the land expansion data, select the processed driver factor folder, set the sampling rate and parallel thread parameters, and calculate the development probability of each land use type.

[0062] In this embodiment, the accuracy verification is obtained in the following way:

[0063] Based on the development probability of each land use type, the final land use data, and the land use quantity demand of historical years, a land transfer matrix, domain weight parameters, and land use demand are set. Through the CA simulation part based on multiple types of random patch seeds, the simulated and predicted historical land use layout is obtained; the land use quantity demand of historical years is obtained from the land use data of historical years.

[0064] To verify the accuracy of land use, the kappa coefficient was used to compare simulated and real land use data. The kappa coefficient was calculated using the Confusion Matrix and FoM functions in the Validation module of the PLUS model. A kappa coefficient above a certain threshold was considered to have passed the accuracy verification. In this example, the overall accuracy was 0.93, and the kappa value was 0.87, indicating high simulation accuracy and passing the accuracy verification.

[0065] Specifically, based on the CARS module in the PLUS model, the land use data at the end of the period is input, and the land transfer matrix and domain weights are set. In the accuracy verification stage, the land use demand is input as the actual land quantity of each land use type in historical years. After setting the parameters, the module is started to obtain the predicted historical land use layout.

[0066] The CA component combines random seed generation and a threshold reduction mechanism to simulate the automatic generation of patches under the constraint of development probability, and the comprehensive probability of land use type k ( This can be expressed by the following formula:

[0067]

[0068] In the formula, This represents the combined probability that grid i changes to land use type k at time t; This represents the development probability of land use type k in grid i; The influence of future demand on land type k is represented by an adaptive inertia mechanism in the PLUS model. This represents the domain impact effect of grid i, i.e., the coverage ratio of land use component k in the next neighborhood.

[0069] In this embodiment, the Markov chain module in the PLUS model is used to predict land use demand under future scenarios. This demand is then adjusted based on a regional land use plan to align with realistic goals. Specifically, future development in this region will place greater emphasis on green land protection and water area control. Therefore, this study reduces the probability of farmland being transferred to construction land by 20%, the probability of forest land being transferred to farmland and construction land by 20% each, and the probability of water area being transferred to construction land by 30%, to obtain the final land use demand for each type. Table 1 below shows the predicted land use demand area for each type in this region in 2030.

[0070] Table 1. Land use demand area for various types in 2030 (km²) 2 )

[0071]

[0072] Specifically, the ecological network composed of ecological source areas and ecological corridors is obtained through the following methods:

[0073] Landscape pattern analysis is conducted on future land use patterns to extract and screen potential ecological source areas, thus obtaining ecological source areas;

[0074] By selecting a resistance factor, the comprehensive resistance surface is calculated using the weighted linear combination method.

[0075] Based on the ecological source area and comprehensive resistance surface, ecological corridors are extracted and combined with the ecological source area to form an ecological network.

[0076] In this embodiment, forest land and grassland are used as foreground elements in landscape pattern analysis, while other land types are used as background. The reclassified binary images are imported into Guidos Toolbox analysis software for processing, and the top 20 core areas with the largest patch areas are selected as potential ecological sources.

[0077] Specifically, potential ecological source areas are extracted and screened based on their potential connectivity index and patch importance index, where patches are considered potential ecological source areas. The specific calculation formula is as follows:

[0078]

[0079] In the formula: n represents the total number of patches; , Let i and j represent the areas of patches i and j, respectively. This represents the maximum probability of a species spreading between patches i and j. The total area of ​​the landscape; denoted by , PC represents the potential connectivity index after removing patch i; dPC represents the potential connectivity index, and dPC represents the patch importance index. In this embodiment, potential ecological source areas with a dPC value greater than 1.5 are selected as ecological source areas.

[0080] Specifically, different resistance factors hinder species migration to varying degrees. In this embodiment, future land use type, elevation, and slope are selected as resistance factors. Each resistance factor is divided into 5 levels. The resistance value of each factor is assigned using the "Reclassification" tool in ArcGIS software. Then, a comprehensive resistance surface is constructed using the "Weighted Sum" tool based on the set weights of each factor. The selected resistance factors and their corresponding weights are shown in Table 2 below.

[0081] Table 2 Relationship between resistance factors and corresponding weights

[0082]

[0083] Specifically, based on the selected ecological source areas and the calculated comprehensive resistance surface, the MCR model is used, and the cost distance and cost path tools in ArcGIS are employed to extract ecological corridors, thereby completing the construction of the ecological network. The MCR model obtains the biological diffusion path as the ecological corridor by calculating the minimum cost resistance path between the ecological source point and the target source point. The specific formula is as follows:

[0084]

[0085] In the formula, This represents the minimum cumulative resistance value; This indicates a positive correlation between minimum cumulative resistance and ecological processes; This represents the cumulative distance and resistance across all grids between grid i and source j. The spatial distance between the two As a resistance. Figure 2 It presents the first-ever prediction of the distribution of ecological sources, resistance surfaces, and ecological corridors in a certain region in 2030.

[0086] Specifically, the re-prediction of land use patterns includes:

[0087] Euclidean distance analysis was performed on the ecological source area and the ecological corridor respectively. The two Euclidean distances were superimposed and then superimposed with the previous generation feedback layer to form a new generation feedback layer.

[0088] The probability of land use type development is corrected based on the new generation feedback layer, and the corrected probability of land use type development is re-input into the PLUS model. Based on the corrected probability of land use type development, the future land use pattern is predicted again.

[0089] Based on the re-predicted future land use patterns, new ecological source areas and ecological corridors are calculated to form a new ecological network. The ecological network specifically consists of ecological source areas and ecological corridors. To determine whether the new iterative ecological network meets the stopping criteria: First, the intersection area ratio of the ecological source areas in the current iteration with that in the previous iteration, and the intersection area ratio of the ecological corridors in the current iteration with those in the previous iteration are calculated. If these ratios are both greater than 90%, the changes in the ecological source areas or ecological corridors are considered minor, and the screening criteria have been met. If these ratios do not reach the threshold, the changing trend of the spatial intersection is observed. When the rate of change of the intersection area is less than the change threshold, the change is considered to be stabilizing, and the iteration is terminated.

[0090] Preferably, this embodiment corrects the probability of construction land development in the land use type development probability based on the feedback layer, as illustrated in the following diagram: Figure 3 As shown.

[0091] Specifically, the formula for constructing the feedback layer in this embodiment is as follows:

[0092]

[0093] In the formula, It is a Euclidean distance function; and The respective Ecological source areas and ecological corridors during the next feedback iteration; and These represent the weights of the source area and the corridor when quantifying the ecological network constraint effect, respectively. The sum of the two is 1. The values ​​can be adjusted according to experience and the situation of the study area. In this embodiment, the general value is taken as 0.5. and These are the feedback layers for the fb_iter and fb_iter-1th feedback iterations, respectively. The probability of land development at grid i obtained by using LEAS in the PLUS model; For the process The probability of land development at grid i, which has been cumulatively corrected in the next iteration, will be used in the next iteration. The land use prediction process in the feedback iteration.

[0094] In this embodiment, the width of the extracted ecological corridor is set in 8 fields, which facilitates the calculation of the intersection area of ​​the ecological source area and the ecological corridor. Figure 4 This example demonstrates the changing trend of the area of ​​the intersection of ecological source areas and ecological corridors during the iterative implementation of this embodiment. In this embodiment, the ecological source areas are updated based on the 2030 land use pattern after the initial feedback correction. Calculations show that the area overlap between the updated ecological source areas and the initial ecological source areas exceeds 90%. Therefore, this study determines the original ecological source areas as the final ecological source areas, and no further selection of ecological source areas is performed in subsequent feedback iterations. The ecological source areas are classified according to their dPC values. This example ultimately undergoes 50 iterations. At this point, the spatial intersection of the ecological network tends to stabilize, and the spatial intersection of the first-level ecological source areas and ecological corridors obtained from each iteration through dPC value classification is ultimately taken as the ecological core area of ​​this example.

[0095] The results show that in the region of this embodiment, urbanization will continue to deepen over the next decade, with the area of ​​built-up land increasing by 200.68 km². 2 This indicates that accurately identifying ecologically valuable core areas and formulating corresponding protection and management policies is an important measure to balance ecological protection and urban development.

[0096] The final distribution map of the ecological core area of ​​a certain region in 2030, as determined in this embodiment, is as follows: Figure 5 As shown, the primary ecological source areas are mostly distributed in large, continuous areas, concentrated in the northern and eastern parts of the region, with a total area of ​​2257.55 km². 2 The core ecological corridors are scattered and discontinuous, mainly interspersed among and connecting primary ecological source areas. After multiple feedback iterations, these ecological corridors were still identified as passable corridors with low resistance values, indicating that they are key areas for the exchange of matter and energy within the study area's ecosystem, and important ecological core areas when future land use changes and ecological network constraints reach dynamic equilibrium.

[0097] Furthermore, this embodiment of the invention also provides an ecological core area identification system based on bidirectional feedback between land use and ecological networks, used to implement the ecological core area identification method based on bidirectional feedback between land use and ecological networks of this invention, such as... Figure 6 As shown, the system includes:

[0098] The data processing module is used to acquire land use data and multi-source driving factor data, and to perform preprocessing.

[0099] The land use pattern prediction module is used to calculate the development probability of land use types based on preprocessed land use data and multi-source driving factor data, and to predict future land use patterns using the PLUS model.

[0100] The ecological network calculation module is used to calculate ecological source areas based on future land use patterns and extract ecological corridors using a minimum resistance model, and to form an ecological network based on ecological source areas and ecological corridors.

[0101] The identification module is used to provide feedback on the ecological network. Specifically, it performs spatial distance analysis on the ecological network to generate a feedback layer, corrects the land use type development probability data of the input PLUS model based on the feedback layer, and then re-predicts the land use pattern. Based on the re-predicted land use pattern, a new ecological network is formed, and a new feedback layer is generated for a new round of feedback. The feedback process is iterated. When the stopping condition is met, the iteration stops, and the area where all ecological networks are superimposed during the iteration process is output to obtain the ecological core area.

[0102] The various modules or mechanisms of the system are mainly used to implement the various steps of the above method embodiments, and will not be described in detail here.

[0103] In addition, this embodiment of the invention also provides a computer storage medium storing a computer program that can be executed by a processor. The computer program executes the ecological core area identification method based on bidirectional feedback of land use and ecological network described above.

[0104] This invention provides a method for identifying ecological core areas based on bidirectional feedback between land use and ecological networks. By combining land use simulation and ecological networks, it deeply integrates the dynamic simulation capabilities of the PLUS model with ecological network construction technology, quantifying the dynamic feedback mechanism between the two. This overcomes the limitations of unidirectional analysis of the relationship between land use and ecological networks in traditional research and effectively improves the accuracy of ecological core area identification. Furthermore, through multiple iterations, it visualizes the dynamic mutual influence effect between the two, extracts the ecological core area in the final state, and enhances the credibility of the obtained ecological core area. Long-term attention to these core areas of species exchange and migration is beneficial to ensuring the connectivity and stability of the ecological network, and helps to achieve regional ecological security pattern protection with limited resources and lower costs.

[0105] Furthermore, in this embodiment of the invention, the preprocessing of land use data and multi-source driving factor data involves projecting both types of data onto a unified projection coordinate system, ensuring that the resolution and spatial range of all data are consistent, facilitating input into the model for calculation, and improving the model's computational efficiency.

[0106] Furthermore, in this embodiment of the invention, the accuracy of the predicted future land use pattern is ensured by first verifying the accuracy of the CARS module of the PLUS model before predicting the future land use pattern.

[0107] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0108] The order of the steps in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0109] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for identifying ecological core areas based on bidirectional feedback between land use and ecological networks, characterized in that, The method includes: Acquire land use data and multi-source driving factor data, and perform preprocessing; The development probability of land use types is calculated based on the preprocessed land use data and multi-source driving factor data, and the future land use pattern is predicted using the PLUS model. Ecological source areas are calculated based on future land use patterns, and ecological corridors are extracted using a minimum resistance model. An ecological network is then formed based on the ecological source areas and ecological corridors. Feedback is provided to the ecological network. Specifically, spatial distance analysis is performed on the ecological network to generate a feedback layer. Based on the feedback layer, the land use type development probability of the input PLUS model is corrected, and the land use pattern is re-predicted. Based on the re-predicted land use pattern, a new ecological network is formed, and a new feedback layer is generated for a new round of feedback. The feedback process is iterated. When the stopping condition is met, the iteration stops, and the area where all ecological networks are superimposed during the iteration process is output to obtain the ecological core area. Specifically, the re-prediction of land use patterns includes: Euclidean distance analysis was performed on the ecological source area and the ecological corridor respectively. The two Euclidean distances were superimposed and then superimposed with the previous generation feedback layer to form a new generation feedback layer. The probability of land use type development is corrected based on the feedback layer, and the corrected probability of land use type development is re-input into the PLUS model to predict the future land use pattern. The ecological network specifically refers to ecological source areas and ecological corridors; the stopping condition specifically refers to the following: the ratio of the intersection area between the ecological source area in the current iteration and the ecological source area in the previous iteration, and the ratio of the intersection area between the ecological corridor in the current iteration and the ecological corridor in the previous iteration both exceed a certain threshold; or the ratio of the intersection area does not exceed a certain threshold, but the rate of change of the intersection area is less than the change threshold.

2. The method for identifying ecological core areas based on bidirectional feedback of land use and ecological networks according to claim 1, characterized in that, Multi-source driving factors specifically include social multi-source driving factors and natural multi-source driving factors; Preprocessing specifically includes: Land use data is reclassified, social multi-source driving factor data is processed using Euclidean distance, and all multi-source driving factor data is projected onto a projection coordinate system consistent with that of land use data. Resampling and masking are then performed to ensure that the resolution and spatial range of all data are consistent.

3. The method for identifying ecological core areas based on bidirectional feedback of land use and ecological networks according to claim 1, characterized in that, Predicting future land use patterns using the PLUS model specifically includes: The processed land use data is converted into a model input format and then input into the model. Based on the PLUS model's land expansion extraction module, land use expansion data for the base period and the end period are obtained. Based on the base and end-stage land use expansion data and the processed multi-source driving factor data, the development probability of land use types is obtained. The land use type development probability is input into the CARS module in the PLUS model to obtain the predicted historical land use layout, and then compared with the actual historical land use layout to verify the accuracy. To obtain future land use demand, the probability of land use type development and future land use demand are input into the CARS module of the PLUS model after accuracy verification, and the predicted future land use pattern is obtained.

4. The method for identifying ecological core areas based on bidirectional feedback of land use and ecological networks according to claim 3, characterized in that, Accuracy verification specifically includes: Based on the development probability of each land use type, the final land use data, and the land use quantity demand in historical years, the simulated and predicted historical land use layout is obtained through the CA simulation part based on multiple types of random patch seeds; the land use quantity demand in historical years is obtained from the land use data. Accuracy is verified by comparing simulated land use predictions with actual land use data using the Kappa coefficient. The Kappa coefficient is calculated based on the confusion matrix and numerical excellence function in the verification module of the PLUS model. When the Kappa coefficient is higher than a certain threshold, the accuracy verification is considered successful.

5. The method for identifying ecological core areas based on bidirectional feedback of land use and ecological networks according to claim 1, characterized in that, The ecological network, composed of ecological source areas and ecological corridors, specifically includes: Landscape pattern analysis is conducted on future land use patterns to extract and screen potential ecological source areas, thereby obtaining ecological source areas; By selecting a resistance factor, the comprehensive resistance surface is calculated using the weighted linear combination method. Based on the ecological source area and comprehensive resistance surface, ecological corridors are extracted and combined with the ecological source area to form an ecological network.

6. The method for identifying ecological core areas based on bidirectional feedback of land use and ecological networks according to claim 5, characterized in that, Potential ecological source areas are extracted and screened based on their potential connectivity index and patch importance index.

7. An ecological core area identification system based on bidirectional feedback between land use and ecological networks, characterized in that, The system includes: The data processing module is used to acquire land use data and multi-source driving factor data, and to perform preprocessing. The land use pattern prediction module is used to calculate the development probability of land use types based on preprocessed land use data and multi-source driving factor data, and to predict future land use patterns using the PLUS model. The ecological network calculation module is used to calculate ecological source areas based on future land use patterns and extract ecological corridors using a minimum resistance model, and to form an ecological network based on ecological source areas and ecological corridors. The identification module is used to provide feedback on the ecological network. Specifically, it performs spatial distance analysis on the ecological network to generate a feedback layer, corrects the land use type development probability data of the input PLUS model based on the feedback layer, and then re-predicts the land use pattern. Based on the re-predicted land use pattern, a new ecological network is formed, and a new feedback layer is generated for a new round of feedback. The feedback process is iterated. When the stopping condition is met, the iteration stops, and the area where all ecological networks are superimposed during the iteration process is output to obtain the ecological core area. Specifically, the re-prediction of land use patterns includes: Euclidean distance analysis was performed on the ecological source area and the ecological corridor respectively. The two Euclidean distances were superimposed and then superimposed with the previous generation feedback layer to form a new generation feedback layer. The probability of land use type development is corrected based on the feedback layer, and the corrected probability of land use type development is re-input into the PLUS model to predict the future land use pattern. The ecological network specifically refers to ecological source areas and ecological corridors; the stopping condition specifically refers to the following: the ratio of the intersection area between the ecological source area in the current iteration and the ecological source area in the previous iteration, and the ratio of the intersection area between the ecological corridor in the current iteration and the ecological corridor in the previous iteration both exceed a certain threshold; or the ratio of the intersection area does not exceed a certain threshold, but the rate of change of the intersection area is less than the change threshold.

8. A computer storage medium, characterized in that, It contains a computer program that can be executed by a processor, which performs the ecological core area identification method based on bidirectional feedback of land use and ecological network as described in any one of claims 1-6.

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