A zoning method and system for realizing the value of natural resource ecological products

Abstract

This invention discloses a zoning method and system for realizing the value of natural resource ecological products, belonging to the technical field of natural resource ecological product value realization. The method first uses a zoning model to iteratively rank the ecological function priorities at the landscape scale of the study area, generating a nested landscape sequence. The model output is analyzed to identify potentially important ecological spaces and high- and low-priority areas. Then, the STARS algorithm is used to determine the grading thresholds for important ecological spaces through mutation testing and to classify them. Next, the SOFM algorithm is used to cluster the supply and demand variables of ecological products to identify clusters of supply and demand relationship characteristics. Finally, based on the identification results of these clusters, functionally similar and spatially adjacent clusters are merged, and the identification results of important ecological spaces are superimposed to obtain the ecological product value realization zoning, and zoning realization strategies and paths are proposed. This method realizes the optimal allocation and protection of ecological resources, the precise formulation of management strategies, and the coordinated green development of the region.

Description

Technical Field

[0001] This invention relates to the field of natural resource ecological product value realization technology, and in particular to a zoning method and system for realizing the value of natural resource ecological products. Background Technology

[0002] As the material foundation for human society's survival and development, natural resources face numerous challenges in realizing the ecological product value, making it difficult to scientifically and efficiently identify and manage the ecological product value of natural resources.

[0003] On the one hand, there is an overemphasis on the functional attributes of ecological units themselves, while completely ignoring the spatial structural importance of landscape units within the overall ecological landscape, as well as their interrelationships with the surrounding environment. This one-sided consideration makes the zoning results unable to accurately reflect the integrity and complexity of the ecosystem, and hinders the rational allocation and protection of ecological resources.

[0004] On the other hand, rule-making is highly subjective, and its accuracy depends heavily on the precision of spatial data. Errors or insufficient precision in spatial data can lead to significant deviations in zoning results, failing to provide a reliable basis for ecological protection and management. For example, some zoning methods based on experience or simple statistics lack scientific quantitative models and objective evaluation standards, making it difficult to accurately grasp the inherent laws and value differences of ecosystems, thus affecting the realization of ecological product value.

[0005] Furthermore, traditional zoning methods cannot comprehensively and accurately identify the supply and demand characteristics of ecological products in different regions, resulting in a lack of pertinence and effectiveness in zoning management strategies.

[0006] In summary, existing technologies have many shortcomings in zoning for the realization of ecological product value, making it difficult to meet the current needs of natural resource management. Therefore, there is an urgent need for a more scientific, objective, and efficient zoning method for realizing the value of natural resource ecological products to address the deficiencies of existing technologies and achieve optimal allocation of ecological resources, restoration of ecological functions, and rational planning of national land space. Summary of the Invention

[0007] The purpose of this invention is to propose a zoning method and system for realizing the value of natural resource ecological products, so as to solve the problem that the zoning of ecological product value realization in the prior art is not scientific and objective enough, and to realize efficient and comprehensive identification and zoning management of the value of natural resource ecological products.

[0008] To achieve the above objectives, this invention proposes a zoning method for realizing the value of natural resource ecological products, the specific steps of which are as follows:

[0009] Step S1: Based on the spatial layout optimization Zonation model, the ecological function priority of the study area at the landscape scale is iteratively sorted to generate a nested landscape sequence with ecological product value ranked from high to low.

[0010] Step S2: Analyze the output of the Zonation model to identify potentially important ecological spaces and high-priority or low-priority areas;

[0011] Step S3: Using the t-test sequential algorithm STARS, the classification threshold of important ecological spaces is determined through mutation testing, and the important ecological spaces are divided.

[0012] Step S4: Based on the STARS mutation test analysis results, extract the top 37% and 21% of the regions in the ecological function priority ranking, respectively, as the general area and core area of ​​important ecological space, and conduct a comparative analysis with the existing ecological protection red line.

[0013] Step S5: Use the Self-Organizing Feature Mapping Network (SOFM) algorithm to perform cluster analysis on the supply and demand variables of important ecological products to identify characteristic clusters of supply and demand relationships of important ecological products;

[0014] Step S6: Based on the identification results of the characteristic clusters of supply and demand relationship of important ecological products, merge ecological function clusters with similar functions and spatial adjacency, and superimpose the identification results of important ecological spaces to obtain the zoning results of ecological product value realization and propose zoning realization strategies and paths.

[0015] Preferably, in step S1, the ecological function priorities of the study area at the landscape scale are iteratively ranked based on the Zonation model. Specific steps include:

[0016] Step S11: Set the number of iterations Calculate the marginal loss of all rasters. ;

[0017] Step S12, according to Assign a specific order to the raster, create a starting point for priority sorting, and begin iterative sorting, where... This represents the raster margin loss of the original data;

[0018] Step S13: Based on the sorting vector constructed in step S12 The grid cells are moved from low priority to high priority, and the marginal loss of the grid cells during the movement is recalculated. And reorder the raster cells according to the updated marginal loss. The marginal loss algorithm uses the core area removal method, and the formula is as follows:

[0019] ;

[0020] in, For grid cells Features The proportion of attribute residuals to the original feature distribution. Features The weight, For feature coverage intensity, take =2, z The number of feature attributes;

[0021] Step S14: Check convergence. When the marginal loss steadily increases as the priority ranking moves, take the result of the last iteration as the final priority ranking.

[0022] Preferably, in step S3, the t-test-based sequential algorithm STARS is determined by a pre-set confidence level. and cutting length Determine the mutation point when the state shift index is... The mutation identification results that reach their maximum value have the highest reliability. The calculation formula is as follows:

[0023] ;

[0024] ;

[0025] in, It is the difference between the means of the two sets of data. Confidence level Below degrees of freedom k Distribution value; For variables The mean squared error; The cutting length; The state offset index; For the first One sample; For variables The average value, For the sample size, This indicates that at a confidence level of Mutation points determined at the horizontal level, q The number of observations.

[0026] Preferably, in step S5, the calculation steps of the SOFM algorithm are as follows:

[0027] Step S51: Assign random values ​​with a range of [0,1] to the connection weights from the input neuron to the output neuron. Determine the learning rate initial value ; Setting up the domain The total number of learning iterations (maximum number of iterations) is ;

[0028] Step S52, Input data vector ,in, For the first One neuron;

[0029] Step S53: Calculate the Euclidean distance between the input vector and the mapping layer weight vector. The formula is as follows:

[0030] ;

[0031] in, For the number of training iterations The first time One input neuron, For the number of training iterations The first time The input neuron and the first The connection weights between each input neuron. The number of neurons;

[0032] Step S54: Find the neuron with the closest interval to the mapping layer weight vector as the winning neuron, and correct all connection weights in the neighborhood using the following formula:

[0033] ;

[0034] ;

[0035] in, For the winning neuron, Features i The weight, The number of neurons in the mapping layer. The change in the weight vector;

[0036] Step S55: Update the learning rate and fields The formula is as follows:

[0037] ;

[0038] ;

[0039] in, For the number of training iterations Learning rate at time For the number of training iterations is The realm of time, Indicates rounding down;

[0040] Step S56: Input a new data vector Repeat steps S53-S55 until... .

[0041] This invention also provides a zoning system for realizing the value of natural resource ecological products, comprising:

[0042] Important ecological space identification module: used to perform priority identification of ecological space delineation based on the Zonation model, analysis of priority identification results, and determination of the hierarchical threshold of important ecological spaces based on the STARS mutation test and extraction of important ecological space identification results;

[0043] Important Ecological Product Supply and Demand Cluster Identification Module: Used to execute the supply and demand cluster identification method based on SOFM algorithm and analyze the identification results;

[0044] Ecological product value realization zoning and realization strategy and path formulation module: used to formulate ecological product value realization zoning and realization strategy and path.

[0045] Therefore, this invention proposes a zoning method and system for realizing the value of natural resource ecological products, the beneficial effects of which are as follows:

[0046] (1) This invention determines the priority and threshold of ecological space by coupling the Zonation model and the STARS algorithm based on landscape connectivity, data spatial distribution characteristics and mutation test, avoiding the problems of subjective division or neglect of the importance of spatial structure in traditional methods, making the identification of important ecological spaces more accurate, and providing reliable support for the optimal allocation of ecological resources and land space planning.

[0047] (2) This invention uses the SOFM algorithm to perform cluster analysis on the supply and demand variables of ecological products, which can effectively identify the complex trade-offs and synergistic relationships between the supply and demand of ecological products in different regions, provide accurate basis for zoning management, and help to formulate more targeted policies for realizing the value of ecological products;

[0048] (3) This invention combines the identification results of important ecological spaces and supply and demand relationship clusters to divide the ecological product value realization into zones, ensuring that the realization strategies and paths are universal internally and differentiated externally, which is conducive to promoting the transformation of regional green development and the modernization of land space governance, and promoting ecological protection.

[0049] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0050] Figure 1 This is a flowchart of a zoning method for realizing the value of natural resource ecological products according to the present invention;

[0051] Figure 2 This is a priority and flexibility ranking diagram of important ecological spaces in a certain lake ecological economic zone according to Embodiment 1 of the present invention; wherein, Figure 2 (a) in the diagram is a schematic diagram of the priority sorting results. Figure 2 (b) in the diagram is a schematic diagram of the flexibility results. Figure 2 (c) in the diagram illustrates downward flexibility. Figure 2 (d) in the diagram illustrates upward flexibility;

[0052] Figure 3 This is a schematic diagram of the priority characteristic curve results of important ecological spaces in Embodiment 1 of the present invention; wherein, Figure 3 (a) in the diagram is a priority ranking of functional coverage. Figure 3 (b) in the diagram is a priority ranking diagram of value coverage;

[0053] Figure 4 This is a schematic diagram illustrating the results of the value mutation test of a regulating ecological product in a certain lake ecological economic zone in Embodiment 1 of the present invention; wherein, Figure 4 (a) in the table represents the first mutation test result of the STARS algorithm. Figure 4 (b) in the image represents the result of the second mutation test. Figure 4 (c) in the figure represents the result of the third mutation test;

[0054] Figure 5 This is a schematic diagram showing the identification results and comparison of important ecological spaces in Embodiment 1 of the present invention; wherein, Figure 5 (a) in the diagram is a schematic diagram of the identification results of important ecological spaces in a certain lake ecological economic zone. Figure 5 (b) in the diagram is a comparison between the identification results of important ecological spaces and the ecological protection red line;

[0055] Figure 6 This is a schematic diagram of the feature value identification results within the important ecological product supply and demand relationship cluster in Embodiment 1 of the present invention; wherein, AS is food supply, CS is carbon sequestration supply, PS is water purification supply, SS is soil conservation supply, WS is water production supply, AD is food demand, CD is carbon demand, PD is water purification demand, SD is soil conservation demand, and WD is water production demand.

[0056] Figure 7 The results of identifying important ecological product supply and demand relationship clusters (codes) and heatmap (counts) in Embodiment 1 of the present invention; wherein, Figure 7 (a) in the diagram illustrates the magnitude of a specific variable value within each cluster. Figure 7 (b) in the diagram illustrates the number of samples in each cluster;

[0057] Figure 8This is a schematic diagram illustrating the identification results of the supply and demand relationship clusters of ecological products in a certain lake ecological economic zone in Embodiment 1 of the present invention; wherein, Figure 8 (a) shows the spatial layout results of 10 supply and demand relationship clusters. Figure 8 (b) represents the internal land use structure of 10 supply and demand clusters. Figure 8 (c) represents the average elevation of the 10 supply and demand relationship clusters. Detailed Implementation

[0058] To make the technical solutions, advantages, and objectives of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below. The described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0059] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0060] Example 1

[0061] like Figure 1 The diagram shows a flowchart of a zoning method for realizing the value of natural resource ecological products according to the present invention. The specific steps are as follows:

[0062] S1. Identification of Important Ecological Spaces.

[0063] Ecological space refers to land space with natural attributes, primarily functioning to provide ecological products or services. It encompasses forests, grasslands, wetlands, rivers, lakes, and tidal flats that require protection and rational utilization. The scientific delineation and rational management of important ecological spaces can provide strong support for the optimal allocation of ecological resources, the restoration of ecological functions, and land spatial planning. This application takes a lake ecological economic zone as an example to identify important ecological spaces and rationally delineate the space for managing the realization of ecological product value, thereby enhancing ecosystem stability.

[0064] S11. Based on the spatial layout optimization zoning model, the ecological function priorities at the landscape scale of the study area are iteratively ranked to generate a nested landscape sequence ranked by ecological product value. The specific iterative steps are as follows:

[0065] S111, Set the number of iterations Calculate the marginal loss of all rasters. ;

[0066] S112, according to Assign a specific order to the raster, create a starting point for priority sorting, and begin iterative sorting, where... This represents the raster margin loss of the original data;

[0067] S113. The sorting vector constructed according to step S112 The grid cells are moved from low priority to high priority, and the marginal loss of the grid cells during the movement is recalculated. And reorder the raster cells according to the updated marginal loss. The marginal loss algorithm uses the core area removal method, and the formula is as follows:

[0068] ;

[0069] in, For grid cells Features The proportion of attribute residuals to the original feature distribution. Features The weight, For feature coverage intensity, take =2, z The number of feature attributes;

[0070] S114. Check convergence. When the marginal loss steadily increases as the priority ranking moves, take the result of the last iteration as the final priority ranking.

[0071] S12. Analyze the output of the Zonation model to identify potentially important ecological spaces and high-priority or low-priority areas;

[0072] The Zonation model ultimately outputs a floating-point raster file with values ​​in the range [0,1], used to describe the spatial distribution of overall protection priorities for a region. Higher raster attribute values ​​indicate greater importance of their ecological functions, thus carrying greater weight when delineating important ecological spaces. For example... Figure 2 As shown in (a), the areas with the highest priority of important ecological spaces in a certain lake ecological economic zone are mainly distributed along the water system, including the coastal areas, the areas around inland lakes, and the inland mountainous areas in the southwest, southeast and northwest; the areas with lower priority are mainly the plains in the central and northern parts.

[0073] like Figure 2 As shown, the flexibility ranking map demonstrates how much the ranking value of each raster unit can be moved up or down without significantly impacting the overall ranking quality. When the raster's flexibility value is 0, it indicates that its ranking value is immutable, and there are no other regional units that can replace that landscape function. The flexibility ranking map can be used to identify potentially important ecological spaces and irreplaceable high-priority or low-priority areas. Figure 2As shown in (b), the overall ecological space flexibility of a certain lake area's ecological economic zone is characterized by high flexibility in the central part and low flexibility on the east and west sides.

[0074] S13. The STARS algorithm based on t-test is adopted to find the mutation points in which the value of regulatory ecological products changes with area through mutation testing. The location of the mutation points is used as the classification threshold of ecological space to divide important ecological spaces.

[0075] The STAR algorithm for t-tests is based on pre-defined confidence levels. and cutting length Determine the mutation point when the state shift index is... The mutation identification results that reach their maximum value have the highest reliability. The calculation formula is as follows:

[0076] ;

[0077] ;

[0078] in, It is the difference between the means of the two sets of data. Confidence level Below degrees of freedom k Distribution value; For variables The mean squared error; The cutting length; The state offset index; For the first One sample; For variables The average value, For the sample size, This indicates that at a confidence level of Mutation points determined at the horizontal level, q The number of observations.

[0079] In ArcGIS, the multi-year average value of ecological product regulation services in the study area is sorted in descending order. The cumulative value proportion carried by different area percentages is calculated to identify abrupt change nodes in the regulation service value as the unit area increases. The corresponding area percentages are then used as the priority protection areas for ecological space. Figure 3 As shown, the priority ranking result output by the Zonation algorithm has homogenized the data, and its characteristic curve cannot fully reflect the priority of the adjustment services in terms of value.

[0080] like Figure 4As shown, the proportion of regulatory ecological products in the total value increases with the increase in the area proportion of the study region's land space. Specifically, when the area proportion is between 5% and 100%, significant abrupt changes occur in the 35%-40% and 75%-80% ranges, with values ​​of 1.23 and 0.59, respectively. Due to the large range, repeated abrupt change tests were performed on the 35%-40% range. The results showed two abrupt change points within this range, located at 37% and 39%, with values ​​of 1.41 and 0.86, respectively. Following the principle of the largest value (highest confidence), the first actual abrupt change location was determined to be 37%, indicating that the important ecological space of a certain lake's ecological economic zone should occupy approximately 37% of its land space. To further identify the core area of ​​the important ecological space, a second abrupt change test analysis was performed on the 5%-35% area range. The results showed a significant abrupt change point when the area proportion increased to 21%, with a value of 1.41, indicating that the core area of ​​the ecological space should occupy approximately 21% of the land area.

[0081] S14. Extract the identification results of important ecological spaces

[0082] The areas representing the top 37% and 21% of ecological function priorities were extracted and designated as the general and core areas of important ecological spaces, respectively. For example... Figure 5 As shown, the important ecological spaces of a certain lake ecological economic zone consist of natural lakes, artificial reservoirs, and surrounding mountainous areas with high vegetation coverage. Regionally, large ecological space patches are concentrated in the southwestern and eastern mountainous areas, as well as the central areas surrounding the lake. The core area is mainly composed of river and lake wetland ecosystems, while a certain number of core patches also exist in remote mountainous areas.

[0083] To further verify the scientific validity and rationality of the identification results, the ecological protection red lines delineated in various regions were overlaid and compared with important ecological spaces. According to local documents, the ecological protection red lines in cities A, B, C, and D account for 22.75%, 16.54%, and 22.42% of their respective administrative areas, which is basically consistent with the important ecological protection core areas delineated in this study in terms of total area. Spatially, the identification results are largely consistent with existing ecological protection red lines. The overall overlap between important ecological spaces and ecological protection red lines is 73.00%, with the ecological protection core area accounting for approximately 67.29% of the overlapping area and the non-core area accounting for 32.74%.

[0084] Looking at individual cities, Table 1 shows that in City A, the overlap between important ecological spaces and ecological red lines is 72.33%, with the core area falling within the ecological red line at 49.74%. City A's ecological protection red line includes the lake area and the southeastern mountain range. The core area of ​​important ecological spaces includes the lake area, the downstream areas of eastern rivers, and higher-altitude areas to the east. General protected areas are distributed in clusters in the southeast, basically covering the existing ecological red line areas. In City C, the overlap between important ecological spaces and ecological red lines is 89.54%, with the core area falling within the ecological red line at 61.11%, indicating a high degree of overall agreement between ecological spaces and red line boundaries. The core protected areas within the southwestern counties generally align with the ecological protection red line. In City B, the overlap between ecological protected areas and red lines is 57.42%, with the core area overlapping at 28.13%. The ecological spaces in the northwestern and eastern river network areas of City B largely match the red line boundaries, but the ecological space identification results do not cover the ecological red lines in the northern and central hilly areas. The overlap between the protected area and the ecological red line in City D is 82.91%, and the overlap in the core area is 75.61%, showing the highest overall consistency. The ecological red line in City D mainly consists of some major rivers, the Northeast Plain, and lakes and reservoirs, which largely matches the identification results. The ecological spatial identification results for Area E cover water bodies such as reservoirs, lakes, and rivers, but do not cover the local forest park ecological red line. Overall, the ecological spatial identification results of this study are basically consistent with the existing spatial distribution of ecological protection red lines. However, due to lower data accuracy and the need to ensure connectivity between various ecological functional patches, the identification results cannot cover those fragmented and scattered ecological red line patches.

[0085] Table 1. Overlap of Important Ecological Spaces and Ecological Red Lines in Four Cities and One District of a Certain Lake Ecological Economic Zone (km) 2 )

[0086] ;

[0087] S2. Identification of supply and demand clusters for important ecological products.

[0088] S21. The Self-Organizing Feature Map Network (SOFM) algorithm is used to perform cluster analysis on the supply and demand variables of important ecological products to identify characteristic clusters of supply and demand relationships for important ecological products. The steps of the SOFM algorithm are as follows:

[0089] Step S211: Assign random values ​​with a range of [0,1] to the connection weights from the input neuron to the output neuron. Determine the learning rate initial value ; Setting up the domain The total number of learning iterations (maximum number of iterations) is ;

[0090] Step S212, Input data vector ,in, For the first One neuron;

[0091] Step S213: Calculate the Euclidean distance between the input vector and the mapping layer weight vector. The formula is as follows:

[0092] ;

[0093] in, For the number of training iterations is The first time One input neuron, For the number of training iterations is The first time The input neuron and the first The connection weights between each input neuron. The number of neurons;

[0094] Step S214: Find the neuron with the closest interval to the mapping layer weight vector as the winning neuron, and correct all connection weights in the neighborhood as follows:

[0095] ;

[0096] ;

[0097] in, For the winning neuron, Features i The weight, The number of neurons in the mapping layer. The change in the weight vector;

[0098] Step S215: Update the learning rate and fields The formula is as follows:

[0099] ;

[0100] ;

[0101] in, For the number of training iterations is Learning rate at time For the number of training iterations is The realm of time, Indicates rounding down;

[0102] Step S216: Input a new data vector Repeat steps S213-S215 until... .

[0103] In this invention, the input samples used for training are the normalized values ​​of 10 sets of feature data, which are the supply and demand of 5 important ecological products at a scale of 61,733 grid points at a scale of 1 km. Therefore, the final output neurons are 10.

[0104] S22. Analysis of the identification results of the supply and demand relationship clusters of important ecological products.

[0105] The SOFM network, trained with 10 neurons, typically possesses one or two dominant functions and several component functions. For example... Figure 6 As shown, the functional characteristics and trade-offs / coordination within each neuron are illustrated. Cluster 1 is dominated by carbon sequestration and food demand, with water production demand as a component function; Cluster 3 is dominated by water production supply and water purification supply, with food, carbon sequestration, and water production demand as component functions. Figure 7 As shown in (a) above, this illustrates the magnitude of specific variable values ​​within each cluster. Figure 7 As shown in (b), the number of samples falling into each cluster is displayed. The results show that clusters 1, 5, 9, and 10 have relatively few grid cells, while cluster 4 has the largest number of samples, i.e., the most widely distributed clustering area. To further identify the spatial distribution of each supply and demand cluster, the clustering results were exported and connected with the 1km fishing net ID used when extracting supply and demand values, resulting in the spatial pattern of the supply and demand relationship clusters of five important ecological products in a certain lake ecological economic zone, as shown in Figure 1. Figure 8 As shown. By combining the supply and demand characteristics of each cluster and their spatial distribution, the features of the 10 clusters can be analyzed.

[0106] Cluster 1: Carbon Sequestration and Food Demand Cluster. This cluster covers approximately 1.47% of the study area, mainly distributed in the periphery of urban built-up areas in various counties and cities. These areas are generally economic development zones and high-tech industrial parks far from the city center, with land use primarily for industrial and mining purposes and relatively high population density. Industrial production activities in these areas have a high demand for water, energy, and food, and the cluster's own supply capacity cannot maintain a supply-demand balance. Therefore, carbon sequestration, food, and water production demands are the dominant characteristics, requiring a large influx of external services to support their socio-economic activities.

[0107] Cluster 2: Water source ecological function cluster. Cluster 2 accounts for 10.67% of the total area and mainly covers most of the surface water in the study area. This cluster has low human activity intensity, high water evaporation, low terrain, and surrounding depressions that are not suitable for crop cultivation. Therefore, the supply and demand of various ecological functions in the Cluster 2 area are at a low level.

[0108] Cluster 3: A water production, purification, and synergistic supply cluster. Cluster 3 covers 7.72% of the study area, with wetlands surrounding surface water bodies as the primary ecosystem type. The land use type is mainly paddy fields, thus Cluster 3 retains a certain food supply capacity. The wetlands in the study area are mainly herbaceous marshes, possessing high water production and purification capabilities. The water production and purification services within the cluster primarily flow into surface water bodies via runoff to fulfill their functions.

[0109] Cluster 4: Grain Production Dominant - Water Purification and Supply Cluster. Cluster 4 is the largest cluster, accounting for 29.83% of the total area. Spatially, Cluster 4 occupies most of the plains in the central and northern parts of the study area, with arable land as the main land use type, providing important grain production functions for the local area. In addition, crop growth provides a certain carbon sequestration function. Large areas of contiguous paddy fields ensure its water production capacity and a certain water purification capacity. However, the soil in the plains is mainly paddy soil, and agricultural planting uses a lot of nitrogen, phosphorus, and potassium fertilizers. In addition, the soil structure is relatively loose, and the loss of nutrients such as nitrogen and phosphorus under rainfall is relatively serious, resulting in a high demand for water purification services. Therefore, Cluster 4 is also one of the key source areas that need to be focused on for non-point source pollution control.

[0110] Cluster 5: Core Demand Cluster for Food Carbon Sequestration. Cluster 5 accounts for only 0.3% of the total study area. Spatially, it is mainly distributed in a point-like pattern in the central urban areas of various counties and cities. This cluster is a typical high-demand, low-supply region with enormous demand for food supply and carbon sequestration services. Its public infrastructure is relatively well-developed, with a high population density and strong socio-economic activity, making it the core development area of ​​many towns. However, it also faces problems such as a fragile ecological environment and severe pollution. With the increasing intensity of land development and the continuous expansion of construction land, the supply and demand contradiction of ecological products within Cluster 5 may further intensify.

[0111] Cluster 6: Water and Food Supply Cluster Dominated by Purification Needs. Cluster 6 accounts for approximately 17.36% of the study area, mainly distributed in patches on the east and west sides of a lake plain and in the hilly areas at its southern end. A small number of Cluster 6 areas are also distributed in strips in the southeastern valley. Functionally, the cluster exhibits the most prominent water purification needs, while its food supply, water production, and purification services are also at a moderate to high level. The cluster is predominantly hilly, with arable land as the main land use. The vegetation cover is primarily composed of crops. The undulating terrain and relatively simple ecological structure result in significant soil nutrient loss through surface water. Cluster 6 should be designated as a key area for soil and water conservation, and comprehensive management measures should be implemented for sloping farmland, erosion gullies, and other land cover with high soil erosion risk to improve the resilience of the local ecosystem.

[0112] Cluster 7: Dominant Carbon Sequestration and Soil and Water Conservation Supply Cluster. Cluster 7 covers approximately 13.19% of the study area, primarily occupying the mountainous valleys in the southwest and southeast of the study area. A small number are also distributed in the northwestern and eastern mountain ranges. Cluster 7 is mostly located on the mountain slopes and valley depressions of various mountain ranges, near water systems, and boasts a superior ecological environment. Its land cover is dominated by dense evergreen broad-leaved forests, with high vegetation coverage; therefore, carbon sequestration is the dominant function in this area. In terms of ecological function importance, Cluster 7 mainly covers most of the important ecological spaces and a small portion of the core areas.

[0113] Cluster 8: A carbon sequestration and soil conservation cluster. Cluster 8 accounts for 15.57% of the total study area, with a large distribution on the upslopes and ridges of the southwestern and northwestern mountainous areas, as well as on the flat slopes at the mountain peaks. A certain area is also present at the mountain tops in the southeast. From an ecological function perspective, Cluster 8 is far from human activity, possesses a robust ecological base, dense vegetation cover, and abundant ecological niches. Therefore, carbon sequestration and soil conservation are the most prominent value contributions of Cluster 8. Cluster 8 encompasses most of the core areas of important ecological protection; therefore, the strictest ecological protection measures are required for this area, prohibiting commercial production and development activities in the forest area, maintaining the authenticity and integrity of the natural landscape, biodiversity, and ecological processes within the region, and ensuring they are free from human interference.

[0114] Cluster 9: Cluster with Soil and Water Loss Demand. Cluster 9 accounts for 3.74% of the total area and is mainly distributed in hilly areas and within the Danxia landform national geological park. These areas are mostly located around the built-up areas of mountainous towns or are special hilly Danxia landforms. Their general characteristics are low vegetation cover and large topographic relief. Although they have a certain carbon sequestration capacity, their risk of soil and water loss is often very serious. The demand for water purification and soil conservation is the most prominent ecological instability feature of these areas.

[0115] Cluster 10: Water production and carbon sequestration demand cluster. Cluster 10 accounts for only 0.15% of the total area and is spatially concentrated in the central urban area in the eastern part of the study area. The ecological function characteristics of Cluster 10 are basically similar to those of Cluster 1, but it is mainly distributed in the central urban area where socio-economic activities are frequent.

[0116] S3. Zoning of Ecological Product Value Realization and Formulation of Zoning Realization Strategies.

[0117] S31. Based on the identification results of the characteristic clusters of supply and demand relationship of important ecological products, merge ecological function clusters with similar functions and spatial adjacency, and superimpose the identification results of important ecological spaces to obtain the zoning results of ecological product value realization and propose zoning realization strategies and paths.

[0118] Starting from the township level, based on the identification results of important ecological product supply and demand relationship clusters, ecological function clusters with similar functional composition and adjacent spatial locations are merged to divide the ecological product value realization zone of a certain lake ecological economic zone. At the township level, based on the characteristics of ecological product supply and demand relationship clusters, the dominant ecological function categories within each township unit are analyzed, and supply and demand clusters with similar land use and supply and demand relationships are merged. The results of important ecological space identification are superimposed, and the final results of the ecological product value realization zoning at the township level are shown in Table 2.

[0119] Table 2. Zoning Results of Ecological Product Value Realization at the Township Level in a Certain Lake Eco-economic Zone

[0120] ;

[0121] Example 2

[0122] This invention also provides a zoning system for realizing the value of natural resource ecological products, comprising:

[0123] Important ecological space identification module: used to perform priority identification of ecological space delineation based on the Zonation model, analysis of priority identification results, and determination of the hierarchical threshold of important ecological spaces based on the STARS mutation test and extraction of important ecological space identification results;

[0124] Important Ecological Product Supply and Demand Cluster Identification Module: Used to execute the supply and demand cluster identification method based on SOFM algorithm and analyze the identification results;

[0125] Ecological product value realization zoning and zoning realization strategy formulation module: used to formulate ecological product value realization zoning, zoning realization strategies and paths.

[0126] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0127] Therefore, this invention provides a zoning method and system for realizing the value of natural resource ecological products. By coupling the zoning model with the STARS algorithm, it achieves accurate identification of important ecological spaces. It uses the SOFM algorithm to analyze the supply and demand relationship clusters of ecological products and to zonify the realization of ecological product value and formulate zoning realization strategies. This solves the problems of strong subjectivity and neglect of spatial structure in existing zoning methods, and realizes the optimal allocation and protection of ecological resources, accurate formulation of management strategies, and regional green and coordinated development.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A zoning method for realizing the value of natural resource ecological products, characterized in that, The specific steps are as follows: Step S1: Based on the spatial layout optimization Zonation model, the ecological function priority of the study area at the landscape scale is iteratively sorted to generate a nested landscape sequence with ecological product value ranked from high to low. Step S2: Analyze the output of the Zonation model to identify potentially important ecological spaces and high-priority or low-priority areas; Step S3: Using the t-test sequential algorithm STARS, the classification threshold of important ecological spaces is determined through mutation testing, and the important ecological spaces are divided. Step S4: Based on the STARS mutation test analysis results, extract the top 37% and 21% of the regions in the ecological function priority ranking, respectively, as the general area and core area of ​​important ecological space, and conduct a comparative analysis with the existing ecological protection red line. Step S5: Use the Self-Organizing Feature Mapping Network (SOFM) algorithm to perform cluster analysis on the supply and demand variables of important ecological products to identify characteristic clusters of supply and demand relationships of important ecological products; Step S6: Based on the identification results of the characteristic clusters of supply and demand relationship of important ecological products, merge ecological function clusters with similar functions and spatial adjacency, and superimpose the identification results of important ecological spaces to obtain the zoning results of the realization of ecological product value and propose zoning realization strategies and paths. In step S3, the t-test-based sequential algorithm STARS is determined by a pre-set confidence level. and cutting length Determine the mutation point when the state shift index is... The mutation identification results that reach their maximum value have the highest reliability. The calculation formula is as follows: ； ； in, It is the difference between the means of the two sets of data. Confidence level Below degrees of freedom k Distribution value; For variables The mean squared error; The cutting length; The state offset index; For the first One sample; For variables The average value, For the sample size, This indicates that at a confidence level of Mutation points determined at the horizontal level, q The number of observations.

2. The zoning method for realizing the value of natural resource ecological products according to claim 1, characterized in that, In step S1, the ecological function priorities of the study area at the landscape scale are iteratively ranked based on the Zonation model. Specific steps include: Step S11: Set the number of iterations Calculate the marginal loss of all rasters. ; Step S12, according to Assign a specific order to the raster, create a starting point for priority sorting, and begin iterative sorting, where... This represents the raster margin loss of the original data; Step S13: Based on the sorting vector constructed in step S12 The grid cells are moved from low priority to high priority, and the marginal loss of the grid cells during the movement is recalculated. And reorder the raster cells according to the updated marginal loss. The marginal loss algorithm uses the core area removal method, and the formula is as follows: ； in, For grid cells Features The proportion of attribute residuals to the original feature distribution. Features The weight, For feature coverage intensity, take =2, z The number of feature attributes; Step S14: Check convergence. When the marginal loss steadily increases as the priority ranking moves, take the result of the last iteration as the final priority ranking.

3. The zoning method for realizing the value of natural resource ecological products according to claim 1, characterized in that, In step S5, the calculation steps of the SOFM algorithm are as follows: Step S51: Assign random values ​​with a range of [0,1] to the connection weights from the input neuron to the output neuron. Determine the learning rate initial value ; Setting up the domain The maximum number of iterations is ; Step S52, Input data vector ,in, For the first One neuron; Step S53: Calculate the Euclidean distance between the input vector and the mapping layer weight vector. The formula is as follows: ； in, For the number of training iterations is The first time One input neuron, For the number of training iterations is The first time The input neuron and the first The connection weights between each input neuron. The number of neurons; Step S54: Find the neuron with the closest interval to the mapping layer weight vector as the winning neuron, and correct all connection weights in the neighborhood using the following formula: ； ； in, For the winning neuron, Features The weight, The number of neurons in the mapping layer. The change in the weight vector; Step S55: Update the learning rate and fields The formula is as follows: ； ； in, For the number of training iterations is Learning rate at time For the number of training iterations is The realm of time, Indicates rounding down; Step S56: Input a new data vector Repeat steps S53-S55 until... .

4. A zoning system for realizing the value of natural resource ecological products, applicable to the zoning method for realizing the value of natural resource ecological products as described in claim 1, characterized in that, include: Important ecological space identification module: used to perform priority identification of ecological space delineation based on the Zonation model, analysis of priority identification results, and determination of the hierarchical threshold of important ecological spaces based on the STARS mutation test and extraction of important ecological space identification results; Important Ecological Product Supply and Demand Cluster Identification Module: Used to execute the supply and demand cluster identification method based on SOFM algorithm and analyze the identification results; Ecological Product Value Realization Zoning Optimization Management Strategy Formulation Module: Used to formulate ecological product value realization zoning optimization management strategies.

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