Natural ecosystem integrity evaluation method and system oriented to national parks

Abstract

The invention discloses a national park-oriented natural ecosystem integrity evaluation method and system, and relates to the technical field of ecological environment monitoring and evaluation, and the method comprises the steps: obtaining historical land utilization data and multi-source spatial data of a target region; according to the historical land utilization data and the multi-source spatial data, constructing a plurality of ecological evolution scenes of the target area; calculating a spatial distribution result of multiple ecological system services according to the ecological system spatial pattern data; determining a mutual relation between the two ecological system services according to the spatial distribution result; according to the method, the current state, the future evolution trend and the scene stability of the ecosystem service relationship can be comprehensively reflected; and technical support can be provided for ecological protection planning, partitioned management and control and long-term monitoring.

Description

Technical Field

[0001] This application relates to the field of ecological environment monitoring and evaluation technology, and in particular to a method and system for evaluating the integrity of natural ecosystems in national parks. Background Technology

[0002] National parks, as the main body of my country's nature reserve system, aim to protect the authenticity and integrity of natural ecosystems. The integrity of natural ecosystems includes not only the integrity of ecosystem structure but also the continuity of ecological processes, the synergy of functions, and the ability to maintain stability and recover under external disturbances. Existing ecological assessment methods for national parks mainly focus on the following aspects: static assessment methods centered on land use structure, landscape pattern, or single ecological elements; functional assessment methods represented by single indicators such as biodiversity, vegetation cover, or habitat quality; and methods that use a specific historical period as a benchmark, lacking comprehensive consideration of future trends and uncertainties.

[0003] The above methods still have significant shortcomings in practical applications: on the one hand, most evaluation methods focus on a single scale or a single time section, making it difficult to comprehensively reflect the complex and variable synergistic and trade-off relationships between ecosystem functions under different scales and time conditions, and thus making it difficult to characterize the overall coordination of ecosystem functions; on the other hand, existing studies generally lack characterization of future scenario changes and system stability, making it difficult to support long-term protection and management decisions for national parks.

[0004] Interrelationships (synergies or trade-offs) among ecosystem services are considered an important entry point for characterizing the coordination of ecosystem functions and the health status of the system. However, existing research mostly remains at the level of correlation analysis or simple coupling analysis, making it difficult to reveal the nonlinear characteristics and spatial heterogeneity of ecosystem service interrelationships under the combined effects of multiple driving factors. In addition, there is a lack of a unified, systematic, and comprehensive evaluation framework, and no technical solution has yet been developed that can directly serve the assessment of the integrity of national park ecosystems. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for evaluating the integrity of natural ecosystems in national parks, which can provide technical support for ecological protection planning, zoning management and long-term monitoring.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for assessing the integrity of natural ecosystems in national parks, the method comprising: Acquire historical land use data and multi-source spatial data for the target area; the multi-source spatial data includes remote sensing image data, traffic data, soil attribute data, socio-economic development data, climate data, and topographic data; Based on the historical land use data and multi-source spatial data, multiple ecological evolution scenarios for the target area are constructed, and the spatial pattern data of the ecosystem of each ecological evolution scenario in the target area under the target evaluation year are obtained through simulation. Among them, the multiple ecological evolution scenarios include at least natural succession scenario, ecological protection scenario, arable land protection scenario, rapid socio-economic development scenario, and coordinated development scenario. The spatial distribution of multiple ecosystem services is calculated based on the aforementioned ecosystem spatial pattern data; wherein, the multiple ecosystem services include water conservation services, soil conservation services, habitat quality services, and carbon sequestration services. The spatial distribution results are used to determine the interrelationships between two ecosystem services, and the interrelationships are used to determine the target spatial scale, which is used to evaluate the target area in the target evaluation year. At the target spatial scale, the strength, trend, and stability characteristics of ecosystem service interrelationships are calculated; wherein, the strength characteristics characterize the strength of ecosystem service interrelationships in the current period; the trend characteristics characterize the direction and magnitude of change of ecosystem service interrelationships in future periods; and the stability characteristics characterize the degree to which ecosystem service interrelationships remain consistent under various evolution scenarios in future periods. The integrity of the natural ecosystem in the target area is comprehensively evaluated based on the intensity characteristics, trend characteristics, and stability characteristics, and the evaluation results of the natural ecosystem integrity are output.

[0007] Secondly, this application provides a natural ecosystem integrity assessment system for national parks, the natural ecosystem integrity assessment system for national parks comprising: The data acquisition module is used to acquire historical land use data and multi-source spatial data of the target area; the multi-source spatial data includes remote sensing image data, traffic data, soil attribute data, socio-economic development data, climate data, and topographic data. The scenario simulation module is used to construct multiple ecological evolution scenarios for the target area based on the historical land use data and multi-source spatial data, and to obtain the ecosystem spatial pattern data of each ecological evolution scenario in the target area under the target evaluation year through simulation; wherein, the multiple ecological evolution scenarios include at least natural succession scenario, ecological protection scenario, arable land protection scenario, rapid socio-economic development scenario, and coordinated development scenario. The ecosystem service assessment module is used to calculate the spatial distribution results of multiple ecosystem services based on the ecosystem spatial pattern data; wherein, the multiple ecosystem services include water conservation services, soil conservation services, habitat quality services, and carbon sequestration services; The multi-scale analysis module is used to determine the interrelationship between two ecosystem services based on the spatial distribution results, and to determine the target spatial scale based on the interrelationship. The target spatial scale is used to evaluate the target area in the target evaluation year. The integrity feature calculation module is used to calculate the intensity, trend, and stability features of ecosystem service interrelationships at the target spatial scale. The intensity feature characterizes the strength of ecosystem service interrelationships in the current period; the trend feature characterizes the direction and magnitude of change in ecosystem service interrelationships in future periods; and the stability feature characterizes the degree to which ecosystem service interrelationships maintain consistency under various evolution scenarios in future periods. The evaluation result output module is used to comprehensively evaluate the integrity of the natural ecosystem of the target area based on the intensity characteristics, trend characteristics and stability characteristics, and output the evaluation results of the natural ecosystem integrity.

[0008] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for assessing the integrity of natural ecosystems for national parks as described above.

[0009] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for assessing the integrity of natural ecosystems for national parks as described above.

[0010] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method for assessing the integrity of natural ecosystems for national parks as described above.

[0011] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method and system for evaluating the integrity of natural ecosystems in national parks. First, it constructs various ecological evolution scenarios using multi-source spatial data as driving factors, and obtains ecosystem spatial pattern data through simulation. Then, it determines the interrelationships between two ecosystem services by calculating the spatial distribution results of multiple ecosystem services. Using these interrelationships as the core evaluation object, it overcomes the limitations of traditional single-indicator or single-factor evaluation methods. Next, it determines the target spatial scale based on the interrelationships and calculates the intensity, trend, and stability characteristics of these interrelationships. Through a comprehensive characterization of the three-dimensional features of "intensity-trend-stability," it achieves a dynamic and systematic representation of the integrity of natural ecosystems, comprehensively reflecting the current state, future evolution trends, and scenario stability of ecosystem service relationships. Finally, it generates natural ecosystem integrity evaluation results, which can be directly used for national park zoning protection, identification of priority ecological restoration areas, and management strategy formulation. These results provide technical support for ecological protection planning, zoning management, and long-term monitoring, and have significant practical application value. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a method for assessing the integrity of natural ecosystems in national parks, as described in one embodiment of this application. Figure 2 This is a schematic diagram of a three-dimensional frame in one embodiment of this application; Figure 3 This is a schematic diagram of the partitioning results in one embodiment of this application. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] like Figures 1 to 2As shown, this application provides a method for assessing the integrity of natural ecosystems in national parks, including the following steps S101 to S106. Wherein: Step S101: Obtain historical land use data and multi-source spatial data for the target area. The multi-source spatial data includes remote sensing image data, traffic data, soil attribute data, socio-economic development data, climate data, and topographic data. The target area includes existing national parks, national parks to be built, nature reserves, important ecological function zones, and their proposed development areas. To meet the data format requirements of the model in subsequent steps, this embodiment performs unified projection, cropping, and spatial resolution matching processing after obtaining the historical land use data and multi-source spatial data to unify the data format of the historical land use data and multi-source spatial data.

[0017] As an optional implementation method, the aforementioned historical land use data and multi-source spatial data were provided by the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, including 6 primary land categories and 25 secondary land categories. Based on the natural endowment, social conditions, and economic development of the study area, various driving factors (multi-source spatial data) were selected to predict future land use scenarios. These driving factors mainly involve climate, topography, basic geographic information, and socio-economic development. The data types of these driving factors are shown in Table 1. The spatial resolution of the land use and topographic data is 30m, while the spatial resolution of the climate and socio-economic data is 1000m. It should be noted that the spatial resolution is not limited to 30m or 1000m. Taking land use data as an example, vector data and raster data can be collected in practical applications. Raster data includes various spatial resolutions such as 10m, 20m, 30m, 300m, 500m, and 1000m. Distances to national, provincial, and county roads, expressways, railways, government seats at all levels, and waterways were calculated using the ArcGIS Euclidean Distance tool. The density of the railway and road networks was calculated using the ArcGIS line density tool. To meet the calculation requirements of the PLUS and InVEST models, all data were standardized in coordinate projection and spatial resolution, for example, 100m.

[0018] Table 1: Data Types of Driving Factors

[0019] Step S102: Construct multiple ecological evolution scenarios for the target area based on the historical land use data and multi-source spatial data, and obtain the ecosystem spatial pattern data of each ecological evolution scenario in the target area under the target evaluation year through simulation; wherein, the multiple ecological evolution scenarios include at least natural succession scenario, ecological protection scenario, arable land protection scenario, rapid socio-economic development scenario, and coordinated development scenario.

[0020] As an optional implementation method, this application uses the PLUS model to simulate the spatial pattern of land use in the study area under future scenarios. The PLUS model integrates the Land Expansion Analysis Strategy (LEAS) and the Cellular Automata (CA) simulation module (CARS) based on multi-type random patch seeds. It can effectively identify the dominant driving factors of different land use types and their influence weights, and reveal the changing patterns and future development directions of land use.

[0021] The PLUS model input data includes historical land use data and multi-source spatial data (driving factors); the PLUS model output data includes land use spatial pattern data under various future (target evaluation year) scenarios, as shown in Table 2: Table 2: Land Use Spatial Pattern Data under Various Future Scenarios

[0022] Step S103: Calculate the spatial distribution results of multiple ecosystem services based on the ecosystem spatial pattern data; wherein, the multiple ecosystem services include water conservation services, soil conservation services, habitat quality services and carbon sequestration services; further, the multiple ecosystem services also include at least one of flood control services, climate regulation services or biodiversity maintenance services.

[0023] As an optional implementation, this application uses the InVEST model to calculate the spatial distribution results of multiple ecosystem services. Specifically, in the calculation of various spatial distribution results, water conservation services are calculated using the Annual Water Yield module of the InVEST model; soil conservation services are calculated using the Sediment Delivery Ratio module of the InVEST model; habitat quality services are calculated using the Habitat Quality module of the InVEST model; and carbon sequestration services are calculated using the Carbon Storage and Sequestration module of the InVEST model. It is worth noting that land use spatial pattern data is an important model input data when calculating the spatial distribution results of ecosystem services. Other parameters are set with reference to the InVEST model manual, published literature, and publicly available carbon density databases.

[0024] Step S104: Determine the interrelationship between two ecosystem services based on the spatial distribution results, and determine the target spatial scale based on the interrelationship. The target spatial scale is used to evaluate the target area under the target evaluation year.

[0025] In step S104 of this application embodiment, determining the interrelationship between two ecosystem services based on the spatial distribution results, and determining the target spatial scale based on the interrelationship, includes the following steps S201 to S203. Wherein: Step S201: Select a spatial scale from a variety of preset spatial scales. The types of spatial scales include grid scale, multi-level sub-basin scale, county scale, city scale, provincial scale, and scale of natural feature formation, etc. Among them, the multi-level sub-basin scale includes 12 sub-basin levels from Level 1 to Level 12; the scale of natural feature formation is a user-defined scale, which means that the study area is divided into homogeneous land units according to rivers, roads, current land use, vegetation distribution, and topography.

[0026] Step S202: For the selected spatial scale, calculate the index coefficient between the two ecosystem services based on the spatial distribution results, and determine the interrelationship between the two ecosystem services based on the index coefficient; wherein, the method for determining the interrelationship between the two ecosystem services is as follows: When the index coefficient is positive, it indicates that the relationship between the two ecosystem services is synergistic; when the index coefficient is negative, it indicates that the relationship between the two ecosystem services is a trade-off.

[0027] As an optional implementation, the index coefficient is the Pearson coefficient. When the Pearson coefficient is positive, it indicates that ecosystem services are synergistic, meaning that two ecosystem services simultaneously increase or decrease. When the Pearson coefficient is negative, it indicates that ecosystem services are trade-offs, meaning that an increase in one ecosystem service is accompanied by a decrease in another. If all ecosystem services (or most ecosystem services) exhibit synergistic relationships, it indicates that the ecosystem is in a benign and sustainable state.

[0028] It should be noted that, as a preferred embodiment, the number of ecosystem services is four. Combining any two ecosystem services yields six possible combinations. Therefore, for the selected spatial scale, six sets of Pearson coefficients need to be calculated, including: Pearson coefficients for water conservation and soil retention services, water conservation and habitat quality services, water conservation and carbon sequestration services, soil conservation and habitat quality services, soil conservation and carbon sequestration services, and habitat quality and carbon sequestration services. The significance of these Pearson coefficients must also be recorded. Furthermore, the ecosystem services in this embodiment may also include at least one of flood control services, climate regulation services, or biodiversity maintenance services. Therefore, in practical applications, the number of ecosystem services is not limited to four; the specific number can be determined based on the natural endowments, policies, and conservation needs of the national park.

[0029] Step S203: For the selected spatial scale, if any two ecosystem services exhibit significant synergistic or trade-off relationships, then that spatial scale is determined to be the target spatial scale. If multiple selected spatial scales are determined to be target spatial scales, then the spatial scale with the largest unit area is selected as the optimal target spatial scale. It should be noted that within the same study area (target region), the smaller the unit area and the more units are divided, the more significant the spatial heterogeneity of ecosystem services becomes, i.e., the more complex the representation and the greater the management difficulty. The optimal scale can both demonstrate significant ecosystem service trade-offs and synergistic relationships and minimize management difficulty; therefore, the spatial scale with the largest unit area is selected as the optimal target spatial scale.

[0030] It should be noted that in step S203 of this application, the method for determining whether the relationship between any two ecosystem services has a significant synergistic or trade-off relationship is as follows: Calculate the t-statistic based on the Pearson coefficient between any two ecosystem services. If the two-sided P-value corresponding to the t-statistic is less than the significance level α, then the relationship between the two ecosystem services is determined to have a significant synergistic or trade-off relationship. The specific value of the significance level α can be determined according to the actual scenario, such as 0.05, 0.01, and 0.001. As a preferred implementation, the significance level α is 0.01. When the two-sided P-value is less than 0.01, it is determined that the two ecosystem services have a significant synergistic or trade-off relationship. Optionally, if any two ecosystem services in the target area cannot meet the threshold requirement of 0.01, the significance level α can be increased from 0.01 to 0.05.

[0031] Step S105: At the target spatial scale, calculate the intensity characteristics, trend characteristics, and stability characteristics of ecosystem service interrelationships; wherein, the intensity characteristics are used to characterize the strength of ecosystem service interrelationships in the current period; the trend characteristics are used to characterize the direction and magnitude of change of ecosystem service interrelationships in the future period; and the stability characteristics are used to characterize the degree to which ecosystem service interrelationships maintain consistency under various evolution scenarios in the future period.

[0032] In one implementation, the current time period is the current time when the prediction task is performed, which is 2020 in this embodiment; the intensity characteristic represents the strength of ecosystem service interrelationships in 2020; the historical time period is the historical time when various types of data are provided to the prediction task, which is from 2005 to 2020 in this embodiment; the future period is the future time in which the prediction target is located in the prediction task, which is 2035 in this embodiment; the trend characteristic represents the changing trend of ecosystem service interrelationships from 2020 to 2035; and the stability characteristic represents the consistency of ecosystem service interrelationships under various scenarios in 2035. It can be understood that the prediction task is to evaluate the integrity of the natural ecosystem of the target area in the future.

[0033] In step S105 of this application embodiment, the calculation of the intensity characteristics, trend characteristics, and stability characteristics of ecosystem service interrelationships includes steps S301 to S303. Wherein: Step S301: Calculate the intensity feature based on the observed values ​​of ecosystem services. The intensity feature characterizes the strength of the interrelationships between ecosystem services in the current time period, with a value range of 0 to 1. The higher the intensity value, the better the synergistic development of ecosystem services. The observed values ​​are the spatial distribution results of ecosystem services. The formula for calculating the intensity feature is: ; ; In the formula, n=2, representing the number of types of ecosystem services. This represents the mean of the normalized values ​​of the two ecosystem services. This represents the normalized value of the i-th ecosystem service system, derived from the observed values ​​of the i-th ecosystem service. Maximum observation value and minimum observation value Calculated.

[0034] Step S302: Calculate the trend feature based on the intensity feature. The trend feature characterizes the direction and magnitude of changes in ecosystem service interrelationships over future periods, expressed as the difference between the average intensity of ecosystem services under various future scenarios and the current intensity. The formula for calculating the trend feature is: ; In the formula, N represents the number of ecological evolution scenarios; This represents the mean of the intensity characteristics of ecosystem services under N different ecological evolution scenarios in the future period; This represents the intensity characteristics of ecosystem service interrelationships under the k-th ecological evolution scenario in future periods; This indicates the intensity characteristics of the interrelationships of ecosystem services in the current period; where the future period is the target year.

[0035] Step S303: Calculate the stability feature based on the intensity feature. The stability feature characterizes the degree to which ecosystem service interrelationships remain consistent across multiple evolution scenarios in the future. The formula for calculating the stability feature is: .

[0036] Step S106: Based on the intensity characteristics, trend characteristics and stability characteristics, conduct a comprehensive evaluation of the integrity of the natural ecosystem in the target area, and output the evaluation results of the natural ecosystem integrity.

[0037] In step S106 of this application embodiment, the comprehensive evaluation of the integrity of the natural ecosystem of the target area based on the intensity characteristics, trend characteristics, and stability characteristics, and the output of the natural ecosystem integrity evaluation result, includes the following steps S401 to S402. Wherein: Step S401: Construct a three-dimensional framework based on the interrelationships of ecosystem services. The three-dimensional framework includes a first dimension, a second dimension, and a third dimension. The first dimension characterizes the strength of the interrelationship between two ecosystem services through strength features. The second dimension characterizes the trend of the interrelationship between two ecosystem services through trend features. The third dimension characterizes the stability of the interrelationship between two ecosystem services through stability features.

[0038] Specifically, in this embodiment of the application, the first dimension characterizes the strength of the relationship between two ecosystem services by intensity features, including: determining whether the intensity feature of the relationship between two ecosystem services is higher than the average value of the intensity features; if the determination result is yes, the relationship between the two ecosystem services is identified as a high-intensity unit; otherwise, it is identified as a low-intensity unit. The second dimension characterizes the trend of the relationship between two ecosystem services through trend features, including: judging whether the trend feature of the relationship between two ecosystem services is greater than or equal to 0. If the judgment result is yes, the relationship between the two ecosystem services is identified as a high-trend unit; otherwise, it is identified as a low-trend unit. The third dimension characterizes the stability of the relationship between two ecosystem services through stability features, including: determining whether the stability feature of the relationship between two ecosystem services is higher than the average value of the stability feature. If the determination result is yes, the relationship between the two ecosystem services is identified as a high-stability unit; otherwise, it is identified as a low-stability unit.

[0039] Step S402: On the target spatial scale, the interrelationships between ecosystem services are divided into preset categories according to the three-dimensional framework to partition any combination of two ecosystem services, and the partitioning results are output. The partitioning results are the natural ecosystem integrity evaluation results.

[0040] As an optional implementation method, such as Figure 2 as well as Figure 3 As shown, Figure 3 The diagram illustrates the partitioning results of eight zones at the Level 9 sub-basin scale, showing the spatial distribution of each zone. The specific partitioning method is as follows: at each target spatial scale, based on this three-dimensional framework, any combination of two ecosystem services is divided into eight zones, namely HHH zone, HHL zone, HLH zone, HLL zone, LHH zone, LHL zone, LLH zone, and LLL zone. It should be noted that in the partitioning of the above combinations, the HHH zone represents a high-intensity unit (H) in the first dimension, a high-trend unit (H) in the second dimension, and a high-stability unit (H) in the third dimension between the two ecosystem services, indicating that the relationship between the two ecosystem services maintains a high-intensity and stable synergistic relationship throughout the period from 2020 to 2035. The LLL zone represents a low-intensity and highly fluctuating synergistic relationship. The HHL zone indicates that the interaction between two ecosystem services is a high-intensity unit (H) in the first dimension, a high-trend unit (H) in the second dimension, and a low-stability unit (L) in the third dimension. The interaction between the two ecosystem services is a high-intensity but unstable synergy. The LLH zone reflects a low-intensity but stable synergy. Other combinations of zones, such as the LHH zone, indicate a situation where the synergy is enhanced and stable; the LHL zone indicates a situation where the synergy is enhanced but fluctuating; the HLH zone indicates a situation where the synergy is weakened but stable; and the HLL zone represents a situation where the synergy is weakened and fluctuating.

[0041] Furthermore, based on the assessment results of natural ecosystem integrity, the potential areas for national park creation are divided into: high-stability, high-intensity sustainable development enhancement areas; high-stability, low-intensity sustainable development enhancement areas; high-stability, high-intensity sustainable development degradation areas; high-stability, low-intensity sustainable development degradation areas; high-intensity sustainable development fluctuation enhancement areas; high-intensity sustainable development fluctuation degradation areas; low-intensity sustainable development fluctuation enhancement areas; and low-intensity sustainable development fluctuation degradation areas. These are further categorized into high-integrity areas, medium-integrity areas, and low-integrity areas. The method for classifying high-integrity, medium-integrity, and low-integrity areas is as follows: The criteria for determining the high integrity zone are: stable system function, high integrity level, and the ability to continuously maintain or improve it. The high integrity zone includes: a high-stability, high-intensity, sustainable development improvement zone; a high-stability, high-intensity, sustainable development degradation zone (although there is a degradation trend, the system foundation is solid, and the integrity remains at a high level); and a high-stability, low-intensity, sustainable development improvement zone (the integrity foundation is relatively low, but the system is stable and has clear potential for repair and improvement). It should be noted that the core of the high integrity zone determination principle is high stability. Even if there are periods of degradation, as long as the system as a whole remains stable, it still falls within the high integrity framework.

[0042] The criteria for determining the intermediate integrity zone are that the system's functions still possess a certain degree of integrity, but its stability or strength is significantly constrained, requiring key management. The intermediate integrity zone is either stable but with a weak foundation and is degrading, or it has a good foundation but the system fluctuates significantly, belonging to a transitional state of "still maintainable, but with risks." The intermediate integrity zone includes: a high-stability, low-intensity, sustainable development degradation zone; a high-intensity, sustainable development, and fluctuation-increasing zone; and a high-intensity, sustainable development, and fluctuation-degrading zone.

[0043] The criteria for determining low-integrity zones are insufficient system stability, susceptibility to ecological disturbances, and difficulty in maintaining integrity. Low-integrity zones include: zones exhibiting low-intensity sustainable development fluctuations and zones exhibiting low-intensity sustainable development fluctuations and degradation. Low-integrity zones are characterized by low intensity and high volatility; even if they experience short-term "improvement," they lack stable support and are therefore priority areas for restoration or strict control.

[0044] For any two ecosystem services, the aforementioned three-dimensional framework is used for partitioning. By integrating the partitioning results of the six ecosystem service combinations, this three-dimensional framework can systematically reveal the spatiotemporal evolution characteristics and dominant types of ecosystem service interrelationships in the study area. Specifically, in this embodiment, the six ecosystem service combinations are water conservation (WY) – soil retention (SR), water conservation (WY) – carbon sequestration (CS), water conservation (WY) – habitat quality (HQ), soil retention (SR) – carbon sequestration (CS), soil retention (SR) – habitat quality (HQ), and carbon sequestration (CS) – habitat quality (HQ), i.e. Figure 3 As shown.

[0045] Furthermore, this application also includes evaluating the results of the natural ecosystem integrity assessment using the XGBoost–SHAP interpretable machine learning framework. The evaluation of the natural ecosystem integrity assessment results includes the following steps S501 to S502. Wherein: Step S501: Use the strength of the relationship between the two ecosystem services as the dependent variable and multi-source spatial data as the independent variable; Step S502: Use the XGBoost model to characterize the nonlinear mapping relationship between the independent variable and the dependent variable; use SHAP values ​​to quantify the marginal contribution and directional influence of the independent variable on the output of the XGBoost model.

[0046] As an optional implementation, the XGBoost–SHAP interpretable machine learning framework can also be replaced by machine learning models such as Boost models, XGBoost models, random forest (RF) models, and Bayesian network models.

[0047] As an application scenario, taking the relationship between soil conservation and habitat quality services as an example, within the target spatial scale, based on comprehensive evaluation and zoning results (such as HHH zone, HHL zone, and LLL zone): In the HHH zone, SHAP analysis results show that increased precipitation has a positive promoting effect on the synergistic development of soil conservation and habitat quality; in the HHL zone, precipitation may have a negative impact on the synergistic relationship between the two, indicating that this region is more sensitive to changes in precipitation; through SHAP dependency plots, the threshold ranges of key factors can be further identified, for example, determining that precipitation within a certain range is most conducive to the synergistic improvement of ecosystem services.

[0048] The results can be overlaid with the comprehensive evaluation level to determine the differences in the main controlling factors of regions with different integrity levels, the limiting factors and optimization paths for improving ecosystem integrity, and to explore the influencing factors and driving mechanisms of the pairwise relationships between ecosystem services.

[0049] Based on the above analysis, the output of the interpretable machine learning framework can be used to verify and explain the comprehensive evaluation results at the mechanistic level, identify the dominant driving factors and sensitive factors of different integrity zones, and provide threshold-based and differentiated control basis for national park zoning management measures. It should be noted that the weights of each driving factor in the interpretable machine learning framework are not pre-set, but are adaptively learned by the model during training, and their contribution is quantified using SHAP values.

[0050] Based on the same inventive concept, this application also provides a natural ecosystem integrity assessment system for national parks, including a data acquisition module, a scenario simulation module, an ecosystem service assessment module, a multi-scale analysis module, an integrity characteristic calculation module, and an assessment result output module. Wherein: The data acquisition module is used to acquire historical land use data and multi-source spatial data of the target area; the multi-source spatial data includes remote sensing image data, traffic data, soil attribute data, socio-economic development data, climate data, and topographic data. The scenario simulation module is used to construct multiple ecological evolution scenarios for the target area based on the historical land use data and multi-source spatial data, and to obtain the ecosystem spatial pattern data of each ecological evolution scenario in the target area under the target evaluation year through simulation; wherein, the multiple ecological evolution scenarios include at least natural succession scenario, ecological protection scenario, arable land protection scenario, rapid socio-economic development scenario, and coordinated development scenario. The ecosystem service assessment module is used to calculate the spatial distribution results of multiple ecosystem services based on the ecosystem spatial pattern data; wherein, the multiple ecosystem services include water conservation services, soil conservation services, habitat quality services, and carbon sequestration services; The multi-scale analysis module is used to determine the interrelationship between two ecosystem services based on the spatial distribution results, and to determine the target spatial scale based on the interrelationship. The target spatial scale is used to evaluate the target area in the target evaluation year. The integrity feature calculation module is used to calculate the intensity, trend, and stability features of ecosystem service interrelationships at the target spatial scale. The intensity feature characterizes the strength of ecosystem service interrelationships in the current period; the trend feature characterizes the direction and magnitude of change in ecosystem service interrelationships in future periods; and the stability feature characterizes the degree to which ecosystem service interrelationships maintain consistency under various evolution scenarios in future periods. The evaluation result output module is used to comprehensively evaluate the integrity of the natural ecosystem of the target area based on the intensity characteristics, trend characteristics and stability characteristics, and output the evaluation results of the natural ecosystem integrity.

[0051] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0052] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0053] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0054] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0055] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0056] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0057] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0058] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for assessing the integrity of natural ecosystems in national parks, characterized in that, The methods for assessing the integrity of natural ecosystems in national parks include: Acquire historical land use data and multi-source spatial data for the target area; the multi-source spatial data includes remote sensing image data, traffic data, soil attribute data, socio-economic development data, climate data, and topographic data; Based on the historical land use data and multi-source spatial data, multiple ecological evolution scenarios for the target area are constructed, and the spatial pattern data of the ecosystem of each ecological evolution scenario in the target area under the target evaluation year are obtained through simulation. Among them, the multiple ecological evolution scenarios include at least natural succession scenario, ecological protection scenario, arable land protection scenario, rapid socio-economic development scenario, and coordinated development scenario. The spatial distribution of multiple ecosystem services is calculated based on the aforementioned ecosystem spatial pattern data; wherein, the multiple ecosystem services include water conservation services, soil conservation services, habitat quality services, and carbon sequestration services. The spatial distribution results are used to determine the interrelationships between two ecosystem services, and the interrelationships are used to determine the target spatial scale, which is used to evaluate the target area in the target evaluation year. At the target spatial scale, the strength, trend, and stability characteristics of ecosystem service interrelationships are calculated; wherein, the strength characteristics characterize the strength of ecosystem service interrelationships in the current period; the trend characteristics characterize the direction and magnitude of change of ecosystem service interrelationships in future periods; and the stability characteristics characterize the degree to which ecosystem service interrelationships remain consistent under various evolution scenarios in future periods. The integrity of the natural ecosystem in the target area is comprehensively evaluated based on the intensity characteristics, trend characteristics, and stability characteristics, and the evaluation results of the natural ecosystem integrity are output.

2. The method for assessing the integrity of natural ecosystems for national parks according to claim 1, characterized in that, The process of determining the interrelationships between two ecosystem services based on the spatial distribution results, and determining the target spatial scale based on the interrelationships, includes: Select one spatial scale from a variety of preset spatial scales, the types of which include grid scale, multi-level sub-basin scale, county scale and city scale; For the selected spatial scale, an index coefficient between the two ecosystem services is calculated based on the spatial distribution results, and the relationship between the two ecosystem services is determined based on the index coefficient; wherein, the method for determining the relationship between the two ecosystem services is as follows: When the index coefficient is positive, it indicates that the relationship between the two ecosystem services is synergistic; when the index coefficient is negative, it indicates that the relationship between the two ecosystem services is a trade-off. For a selected spatial scale, if the relationship between any two ecosystem services has a significant synergistic or trade-off relationship, then the spatial scale is determined to be the target spatial scale; if multiple selected spatial scales are determined to be target spatial scales, then the spatial scale with the largest unit area is selected as the optimal target spatial scale.

3. The method for assessing the integrity of natural ecosystems in national parks according to claim 2, characterized in that, The computational analysis of the strength, trend, and stability characteristics of ecosystem service interrelationships includes: The intensity characteristic is calculated based on observations of ecosystem services, where the observations represent the spatial distribution of ecosystem services; the formula for calculating the intensity characteristic is: ； ； In the formula, n=2, representing the number of types of ecosystem services; This represents the mean of the normalized values ​​of the two ecosystem services. This represents the normalized value of the i-th ecosystem service system, derived from the observed values ​​of the i-th ecosystem service. Maximum observation value and minimum observation value Calculated; The trend feature is calculated based on the intensity feature, and the formula for calculating the trend feature is as follows: ； In the formula, N represents the number of ecological evolution scenarios; This represents the mean of the intensity characteristics of ecosystem services under N different ecological evolution scenarios in the future period; This represents the intensity characteristics of ecosystem service interrelationships under the k-th ecological evolution scenario in future periods; This indicates the intensity of the interrelationships among ecosystem services during the current period. The stability feature is calculated based on the strength feature, and the formula for calculating the stability feature is as follows: 。 4. The method for assessing the integrity of natural ecosystems in national parks according to claim 3, characterized in that, The comprehensive evaluation of the natural ecosystem integrity of the target area based on the intensity characteristics, trend characteristics, and stability characteristics, and the output of the natural ecosystem integrity evaluation results, include: A three-dimensional framework based on the interrelationships of ecosystem services is constructed. The three-dimensional framework includes a first dimension, a second dimension, and a third dimension. The first dimension characterizes the strength of the interrelationship between two ecosystem services through strength features. The second dimension characterizes the trend of the interrelationship between two ecosystem services through trend features. The third dimension characterizes the stability of the interrelationship between two ecosystem services through stability features. At the target spatial scale, the interrelationships between ecosystem services are divided into preset categories according to the three-dimensional framework to partition any combination of two ecosystem services, and the partitioning results are output. The partitioning results are the natural ecosystem integrity assessment results.

5. The method for assessing the integrity of natural ecosystems in national parks according to claim 4, characterized in that, The first dimension characterizes the strength of the relationship between two ecosystem services through intensity features, including: determining whether the intensity feature of the relationship between the two ecosystem services is higher than the average value of the intensity features; if the result is yes, the relationship between the two ecosystem services is identified as a high-intensity unit, otherwise it is identified as a low-intensity unit. The second dimension characterizes the trend of the relationship between two ecosystem services through trend features, including: determining whether the trend feature of the relationship between the two ecosystem services is greater than or equal to 0; if the result is yes, the relationship between the two ecosystem services is identified as a high-trend unit, otherwise it is identified as a low-trend unit. The third dimension characterizes the stability of the relationship between two ecosystem services through stability features, including: determining whether the stability feature of the relationship between the two ecosystem services is higher than the average value of the stability features; if the result is yes, the relationship between the two ecosystem services is identified as a high-stability unit, otherwise it is identified as a low-stability unit.

6. The method for assessing the integrity of natural ecosystems for national parks according to claim 1, characterized in that, It also includes evaluating the results of natural ecosystem integrity assessments using the XGBoost–SHAP interpretable machine learning framework, wherein the evaluation of natural ecosystem integrity assessment results includes: The strength of the interrelationship between the two ecosystem services is used as the dependent variable, and multi-source spatial data are used as the independent variable. The XGBoost model is used to characterize the nonlinear mapping relationship between the independent and dependent variables; the SHAP value is used to quantify the marginal contribution and directional influence of the independent variables on the output of the XGBoost model.

7. A natural ecosystem integrity assessment system for national parks, characterized in that, The natural ecosystem integrity assessment system for national parks includes: The data acquisition module is used to acquire historical land use data and multi-source spatial data of the target area; the multi-source spatial data includes remote sensing image data, traffic data, soil attribute data, socio-economic development data, climate data, and topographic data. The scenario simulation module is used to construct multiple ecological evolution scenarios for the target area based on the historical land use data and multi-source spatial data, and to obtain the ecosystem spatial pattern data of each ecological evolution scenario in the target area under the target evaluation year through simulation; wherein, the multiple ecological evolution scenarios include at least natural succession scenario, ecological protection scenario, arable land protection scenario, rapid socio-economic development scenario, and coordinated development scenario. The ecosystem service assessment module is used to calculate the spatial distribution results of multiple ecosystem services based on the ecosystem spatial pattern data; wherein, the multiple ecosystem services include water conservation services, soil conservation services, habitat quality services, and carbon sequestration services; The multi-scale analysis module is used to determine the interrelationship between two ecosystem services based on the spatial distribution results, and to determine the target spatial scale based on the interrelationship. The target spatial scale is used to evaluate the target area in the target evaluation year. The integrity feature calculation module is used to calculate the intensity, trend, and stability features of ecosystem service interrelationships at the target spatial scale. The intensity feature characterizes the strength of ecosystem service interrelationships in the current period; the trend feature characterizes the direction and magnitude of change in ecosystem service interrelationships in future periods; and the stability feature characterizes the degree to which ecosystem service interrelationships maintain consistency under various evolution scenarios in future periods. The evaluation result output module is used to comprehensively evaluate the integrity of the natural ecosystem of the target area based on the intensity characteristics, trend characteristics and stability characteristics, and output the evaluation results of the natural ecosystem integrity.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for assessing the integrity of natural ecosystems for national parks as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for assessing the integrity of natural ecosystems for national parks as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for assessing the integrity of natural ecosystems for national parks as described in any one of claims 1-6.

Images