Partitioning method and system for realizing value of natural resource ecological product

Abstract

The invention specifically discloses a zoning method and system for natural resource ecological product value implementation, and relates to the technical field of natural resource ecological product value implementation. The method comprises the following steps: firstly, iteratively sorting ecological function priorities under a landscape scale of a research area based on a Zonalization model, generating a landscape nested sequence, analyzing a model output result, and identifying a potential important ecological space and high and low priority areas; then, an STARS algorithm is adopted to determine an important ecological space grading threshold value through mutation test and divide the important ecological space grading threshold value; carrying out the clustering analysis of the supply and demand variables of the ecological product through an SOFM algorithm, and recognizing a supply and demand relation feature cluster; and finally, combining clusters with similar functions and adjacent spaces according to supply and demand relationship feature cluster identification results, and superposing important ecological space identification results to obtain ecological product value implementation partitions and propose partition implementation strategies and paths. According to the method, optimal configuration and protection of ecological resources, accurate formulation of management strategies and green collaborative development of regions are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of natural resource ecological product value realization, and in particular to a natural resource ecological product value realization zoning method and system. BACKGROUND

[0002] As the material basis for the survival and development of human society, the ecological product value realization zoning of natural resources faces many challenges, making it difficult to scientifically and efficiently realize the identification and management of natural resource ecological product value.

[0003] On the one hand, excessive emphasis on the functional attributes of ecological units completely ignores the importance of spatial structure of landscape units in the entire ecological landscape and their mutual relationship with the surrounding environment. This one-sided consideration makes the zoning results unable to accurately reflect the integrity and complexity of the ecological system, making it difficult to achieve rational allocation and protection of ecological resources.

[0004] On the other hand, the rules are highly subjective in terms of accuracy, and their accuracy is highly dependent on the accuracy of spatial data. Once there are errors or insufficient accuracy in spatial data, the zoning results will deviate greatly, and cannot provide reliable basis for ecological protection and management. For example, in some zoning methods based on experience or simple statistics, due to the lack of scientific quantitative models and objective evaluation standards, it is difficult to accurately grasp the internal laws and value differences of the ecological system, thereby affecting the effect of ecological product value realization.

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

[0006] In summary, the existing technology has many defects in ecological product value realization zoning, and it is difficult to meet the current needs of natural resource management. Therefore, a more scientific, objective and efficient natural resource ecological product value realization zoning method is needed to solve the problems in the existing technology and achieve optimal allocation of ecological resources, ecological function restoration and rational planning of land space. SUMMARY

[0007] The purpose of the present application is to provide a natural resource ecological product value realization zoning method and system to solve the problem of insufficient scientific and objective ecological product value realization zoning in the prior art, and to achieve efficient and comprehensive identification and zoning management of natural resource ecological product value.

[0008] To achieve the above-mentioned purpose, the present application provides a natural resource ecological product value realization zoning method, the specific steps of which are as follows: Step S1: Iteratively sort the ecological function priorities at the landscape scale of the study area based on the spatial layout optimization zonation model to generate a landscape nested sequence ranked by ecological product value; 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 to determine the classification threshold of the important ecological space through mutation test, and divide the important ecological space; Step S4: Based on the results of the STARS mutation test analysis, the top 37% and 21% areas in the ecological function priority ranking are extracted as the general area and core area of ​​the important ecological space, respectively, and the areas are superimposed and compared with the existing ecological protection red line; Step S5: Using the self-organizing feature map network algorithm SOFM to perform cluster analysis on the supply and demand variables of important ecological products, and identify characteristic clusters of the supply and demand relationship of important ecological products; Step S6: Based on the identification results of the characteristic clusters of the supply and demand relationships of important ecological products, merge the ecological function clusters with similar functions and spatial proximity, and superimpose the important ecological space identification results to obtain the ecological product value realization zoning results and propose zoning realization strategies and paths.

[0009] Preferably, in step S1, the ecological function priorities at the landscape scale of the study area are iteratively ranked based on the zonation model, and the specific steps include: Step S11: Set the number of iterations , calculate the marginal loss of all grids ; Step S12: Assigning a specific order to the grid creates a starting point for priority sorting and begins iterative sorting, where is the grid marginal loss of the original data; Step S13: Sorting vector constructed according to step S12 , move from low priority to high priority grid cells, and calculate the marginal loss of grid cells during the movement again , and reorder the grid cells according to the updated marginal loss ; The marginal loss algorithm adopts the core area removal method, and the formula is as follows: ; in, Grid cells Features The proportion of attribute residual to the original distribution of features, Features The weight of is the feature coverage strength, take =2, z is the number of characteristic attributes; Step S14: Check convergence. When the marginal loss increases steadily as the priority ranking moves, take the result of the last round of iteration as the final priority ranking.

[0010] Preferably, in step S3, the sequential algorithm STARS based on t-Test is determined by a pre-set confidence level. and cut length Determine the mutation point when the state deviation index The credibility of the mutation identification result is the highest when it reaches the maximum, and the calculation formula is as follows: ; ; in, is the difference between the mean values ​​of the two groups of data. Is the confidence level The following has degrees of freedom k distribution value; For variables The mean square error of To cut to length; is the state offset index; For the samples; For variables The average value of is the sample size, Indicates that the confidence level is The mutation point determined at the level, q is the number of observations.

[0011] Preferably, in step S5, the calculation steps of the SOFM algorithm are as follows: Step S51: Assign a random value in the range [0,1] to the connection weight from the input neuron to the output neuron. , determine the learning rate Initial value of ; Set the field , the total number of learning times (maximum number of iterations) is ; Step S52: Input data vector ,in, For the neurons; Step S53: Calculate the Euclidean distance between the input vector and the mapping layer weight vector , the formula is as follows: ; wherein, is the input neuron number of the training iteration number of ; is the input neuron number of the training iteration number of ; is the connection weight value between the input neuron number of the training iteration number of ; is the input neuron number of the training iteration number of ; Step S54, finding the neuron closest to the mapping layer weight vector as the winning neuron, correcting all connection weights in the field, the formula is as follows: ; ; wherein, is the winning neuron, is the weight of the feature i ; is the number of neurons in the mapping layer, is the weight vector change amount; Step S55, updating the learning rate and the field , the formula is as follows: ; ; wherein, is the learning rate of the training iteration number of ; is the field of the training iteration number of ; represents the floor; Step S56, inputting a new data vector , repeating steps S53-S55 until .

[0012] The application also provides a natural resource ecological product value realization zoning system, comprising: An important ecological space identification module: used for performing ecological space zoning priority identification based on a Zonation model, priority identification result analysis, and important ecological space grading threshold division and important ecological space identification result extraction based on STARS mutation test; An important ecological product supply and demand relationship cluster identification module: used for performing a supply and demand relationship cluster identification method based on a SOFM algorithm and identification result analysis; The ecological product value realization zoning and realization strategy and path making module is used for making the ecological product value realization zoning and realization strategy and path.

[0013] Therefore, the natural resource ecological product value realization zoning method and system has the following beneficial effects: (1) The ecological space priority and threshold are determined based on the landscape connectivity, data space distribution characteristics and mutation test by coupling the Zonation model and the STARS algorithm, the problems of subjective division or neglecting the importance of spatial structure in the traditional method are avoided, the important ecological space identification is more accurate, and reliable support is provided for ecological resource optimization allocation and land space planning; (2) The SOFM algorithm is used for clustering analysis of the ecological product supply and demand variables, the complex trade-off and synergistic relationship of ecological product supply and demand in different regions can be effectively identified, accurate basis is provided for zoning management, and more targeted ecological product value realization policies are helped to make; (3) The ecological product value realization zoning is carried out in combination with the important ecological space and supply and demand relationship cluster identification results, the realization strategy and path have universality inside and difference outside, which is beneficial to promote regional green development transformation and land space modernization governance, and promote ecological protection.

[0014] The technical solutions of the present application will be further described in detail below with the help of the drawings and examples. DESCRIPTION OF DRAWINGS

[0015] Figure 1 The flowchart of the natural resource ecological product value realization zoning method of the present application; Figure 2 The important ecological space priority and flexibility ranking diagram of a certain lake ecological economic zone in example one of the present application; wherein, Figure 2 (a) in the above is a priority ranking result schematic diagram, Figure 2 (b) in the above is a flexibility result schematic diagram, Figure 2 (c) in the above is a downward flexibility schematic diagram, Figure 2 (d) in the above is an upward flexibility schematic diagram; Figure 3 The important ecological space priority feature curve result schematic diagram in example one of the present application; wherein, Figure 3 (a) in the above is a priority ranking schematic diagram of function coverage rate, Figure 3 (b) in the above is a priority ranking schematic diagram of value coverage rate; Figure 4 The regulation type ecological product value mutation test result schematic diagram of a certain lake ecological economic zone in example one of the present application; wherein, Figure 4 (a) in the above is the first mutation test result of the STARS algorithm,Figure 4 (b) is the result of the second mutation test. Figure 4 (c) in the figure is the result of the third mutation test; Figure 5 The following is a schematic diagram showing the results of the identification of important ecological spaces and a comparison diagram in Example 1 of the present invention; wherein, Figure 5 (a) is a schematic diagram of the identification results of important ecological spaces in a lake ecological and economic zone. Figure 5 (b) is a schematic diagram comparing the identification results of important ecological spaces and the ecological protection red line; Figure 6 Schematic diagram of the result of identifying characteristic values ​​within the cluster group of the supply and demand relationship of important ecological products in Example 1 of the present invention; wherein AS represents food supply, CS represents carbon sequestration supply, PS represents water purification supply, SS represents soil conservation supply, WS represents water production supply, AD represents food demand, CD represents carbon demand, PD represents water purification demand, SD represents soil conservation demand, and WD represents water production demand; Figure 7 This is the identification result of the supply and demand relationship cluster (codes) and heat map (counts) of important ecological products in Example 1 of the present invention; Figure 7 (a) is a schematic diagram of the size of the specific variable value within each cluster. Figure 7 (b) is a schematic diagram of the number of samples in each cluster; Figure 8 This is a schematic diagram of the results of identifying the supply and demand relationship clusters of ecological products in a lake ecological economic zone in Example 1 of the present invention; wherein, Figure 8 (a) is the spatial layout result of 10 supply and demand relationship clusters. Figure 8 (b) is the internal land use structure of the 10 supply and demand relationship clusters. Figure 8 (c) in the figure is the average elevation of the 10 supply and demand relationship clusters. DETAILED DESCRIPTION

[0016] To make the technical solutions, advantages, and objectives of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below. The described embodiments are part of the embodiments of the present invention, not all of them. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0018] Example 1 like Figure 1FIG. 1 is a flow chart of a zoning method for realizing the value of natural resource ecological products according to the present invention. The specific steps are as follows: S1. Identification of important ecological spaces.

[0019] Ecological space refers to land space with natural attributes and the main function of providing ecological products or ecological services. It covers forests, grasslands, wetlands, rivers, lakes, mudflats, etc. that need to be protected and rationally utilized. Scientific demarcation and reasonable management and control of important ecological spaces can provide strong support for the optimal allocation of ecological resources, restoration of ecological functions, and land space planning. This application takes a certain lake ecological economic zone as an example to identify important ecological spaces and reasonably divide the space for realizing the value of ecological products and improve the stability of the ecosystem.

[0020] S11. Based on the spatial layout optimization zonation model, the ecological function priorities at the landscape scale of the study area were iteratively ranked to generate a nested sequence of landscapes ranked by ecological product value. The specific iterative steps are as follows: S111. Set the number of iterations , calculate the marginal loss of all grids ; S112, according to Assigning a specific order to the grid creates a starting point for priority sorting and begins iterative sorting, where is the grid marginal loss of the original data; S113, sorting vector constructed according to step S112 , move from low priority to high priority grid cells, and calculate the marginal loss of grid cells during the movement again , and reorder the grid cells according to the updated marginal loss ; The marginal loss algorithm adopts the core area removal method, and the formula is as follows: ; in, Grid cells Features The proportion of attribute residual to the original distribution of features, Features The weight of is the feature coverage strength, take =2, z is the number of characteristic attributes; S114. Check convergence. When the marginal loss increases steadily as the priority ranking moves, take the result of the last round of iteration as the final priority ranking.

[0021] S12. Analyze the output of the zonation model to identify potentially important ecological spaces and high-priority or low-priority areas; The final output of Zonation model contains a floating-point grid file with a value range of [0, 1] to describe the spatial distribution of the overall protection priority of the region. The higher the grid attribute value, the more important the ecological function, and the greater the weight in the division of important ecological space. As shown in (a) of FIG. 1, the region with the highest priority of important ecological space in the lake area ecological economic zone is mainly distributed along the water system, including the coastal area and the surrounding area of inland lakes, as well as the inland mountainous areas in the southwest, southeast and northwest. The lower priority areas are mainly in the central north plain. Figure 2

[0022] As shown in FIG. 2, the flexibility ranking chart shows how much the ranking value of each grid cell can be moved up or down without causing significant loss to the overall ranking quality. When the flexibility value of the grid is 0, it means that the order value cannot be changed, and there is no other area cell that can replace the landscape function. The flexibility ranking chart can be used to identify potential important ecological space and irreplaceable high-priority or low-priority areas. Figure 2 Figure 2 As shown in (b) of FIG. 1, the ecological space flexibility of the lake area ecological economic zone as a whole is high in the middle and low on the east and west sides.

[0023] S13, using the sequential algorithm STARS of t-Test to find the mutation point of the adjustment type ecological product value changing with the area through mutation test, taking the mutation point position as the grading threshold of ecological space, and dividing the important ecological space; The sequential algorithm STARS of t-Test is determined by the preset confidence level and the cut-off length The mutation point is determined, and when the state shift index reaches the maximum, the confidence level of the mutation identification result is the highest, and the calculation formula is as follows: ; ; Wherein, is the difference between the average values of the two groups of data, is the confidence level with degrees of freedom k distribution value; is the mean square error of the variable ; is the cut-off length; is the state shift index; is the th sample; is the average value of the variable , is the sample size,​​ indicates the mutation point determined at a confidence level of 0.05, q is the number of observations.

[0024] The results of the average annual value of the ecological product regulation service of the study area in ArcGIS are sorted in descending order. The cumulative value proportion carried by different area proportions is calculated to seek the mutation node in the process of the regulation service value increasing with the unit area. The corresponding area proportion is taken as the priority protection range of the ecological space. As shown in Figure 3 , the priority ranking result output by the Zonation algorithm uniformly processes the data, and the characteristic curve cannot fully reflect the priority of the regulation service in value.

[0025] As shown in Figure 4 , with the increase of the proportion of land space in the study area, the proportion of the value of the regulation type ecological product in the total value shows an upward trend. Among them, when the area proportion is 5%-100%, significant upward mutation points appear in the 35%-40% and 75%-80% intervals, with values of 1.23 and 0.59 respectively. Due to the large interval span, the 35%-40% area is re-tested for mutation, and the results show that there are two mutation points in this interval, located at 37% and 39%, with values of 1.41 and 0.86 respectively. According to the principle of the largest value (highest reliability), the first actual mutation position is determined to be 37%, indicating that the important ecological space of the certain lake ecological economic zone should account for about 37% of the land space area. In order to further identify the core area of the important ecological space, the 5%-35% area interval is re-tested for mutation analysis. The results show that a significant upward mutation point appears when the area proportion rises to 21%, with a value of 1.41, indicating that the core area of the ecological space should account for about 21% of the land area.

[0026] S14, extracting important ecological space identification results The areas with priority ranking of 37% and 21% of the ecological function are extracted as the general area and core area of the important ecological space. As shown in Figure 5 , the important ecological space of the certain lake ecological economic zone is composed of natural lakes, artificial reservoirs and peripheral mountainous areas with high vegetation coverage. In terms of regions, large ecological space patches are concentrated in the southwestern and eastern mountainous areas and the central lake area. Among them, the core area is mainly composed of river and lake wetland ecosystems, and there are also a certain number of core patches in remote mountainous areas.

[0027] To further verify the scientificity and rationality of the identification results, the ecological protection red line range and important ecological space in each region were superimposed and compared. According to the files of each region, the ecological protection red line of A city, B city, C city and D city accounted for 22.75%, 16.54%, 22.42% of the administrative area, respectively, which was basically consistent with the total area of the important ecological protection core area identified in this study. From the spatial distribution, the identification results were basically consistent with the existing ecological protection red line. The overall overlap ratio of important ecological space and ecological protection red line was 73.00%, and the core area accounted for 67.29% of the overlap area, and the non core area accounted for 32.74%.

[0028] From Table 1, the overlap ratio of important ecological space and ecological red line in A city was 72.33%, of which the core area accounted for 49.74% of the ecological red line. The ecological protection red line of A city includes the lake area and the southeast mountainous area. The core area of important ecological space includes the lake area, the downstream area of eastern rivers and the high altitude area on the east side. The general protection space is distributed in a lump shape in the southeast, which basically covers the existing ecological red line area. The overlap ratio of important ecological space and ecological red line in C city was 89.54%, of which the core area accounted for 61.11% of the ecological red line. The overall consistency of ecological space and red line range is high. The core protection area in the southwest county is consistent with the general trend of ecological protection red line. The overlap ratio of ecological protection area and red line in B city was 57.42%, and the core area overlap ratio was 28.13%. The ecological space in the northwest and eastern river network area of B city is consistent with the red line range, but the identification result of ecological space does not cover the ecological red line in the northern and central hilly areas. The overlap ratio of protection area and red line in D city was 82.91%, and the core area overlap ratio was 75.61%, with the highest overall consistency. The ecological red line of D city is mainly composed of some main rivers, northeast plain and lakes and reservoirs, which is basically consistent with the identification result. The ecological space identification result of E area covers reservoirs, lakes, rivers and other water areas, but does not cover the forest park ecological red line in the local area. In general, the ecological space identification result of this study is basically consistent with the spatial distribution of the existing ecological protection red line, but due to the low data accuracy, it is difficult to cover the scattered ecological red line patches in order to protect the connectivity between ecological function patches.

[0029] Table 1 Overlap of important ecological space and ecological red line in four cities and one district of a lake ecological economic zone (km 2 ) ;

[0030] S2, important ecological product supply and demand relationship cluster identification.

[0031] S21. Use the self-organizing feature map network algorithm (SOFM) to perform cluster analysis on the supply and demand variables of important ecological products and identify characteristic clusters of the supply and demand relationships of important ecological products. The steps of the SOFM algorithm are as follows: Step S211: Assign a random value in the range [0,1] to the connection weight from the input neuron to the output neuron. , determine the learning rate Initial value of ; Set the field , the total number of learning times (maximum number of iterations) is ; Step S212: Input data vector ,in, For the neurons; Step S213: Calculate the Euclidean distance between the input vector and the mapping layer weight vector , the formula is as follows: ; in, The number of training iterations is The first input neurons, The number of training iterations is The first The input neuron The connection weights between the input neurons, is the number of neurons; Step S214: Find the neuron closest to the mapping layer weight vector as the winning neuron, and correct all connection weights in the area. The formula is as follows: ; ; in, For the winning neuron, Features i The weight of is the number of neurons in the mapping layer, is the weight vector change; Step S215: Update learning rate and fields , the formula is as follows: ; ; in, The number of training iterations is The learning rate when The number of training iterations is The field of time, denotes rounding down; Step S216, input a new data vector , repeat steps S213-S215 until .

[0032] The input sample for training in the present application is the normalized value of 10 groups of feature data of 61733 grid points of 5 important ecological products at the scale of 1 km, so the number of final output neurons is 10.

[0033] S22, important ecological product supply and demand relationship cluster identification result analysis.

[0034] The 10 neurons obtained by training the SOFM network generally have one or two dominant functions and several group functions. As shown in Figure 6 , the function characteristics and trade-off / synergy within each neuron are shown. Cluster 1 is dominated by carbon sequestration demand and food demand, and water production demand is a group function; cluster 3 is dominated by water supply and water purification supply, and food, carbon sequestration, and water demand are group functions. As shown in Figure 7 (a), the size of the specific variable value within each cluster is shown. As shown in Figure 7 (b), the number of samples falling into each cluster is shown, and the results show that clusters 1, 5, 9, and 10 have relatively fewer grids, and cluster 4 has the most samples, i.e., the most widely distributed clustering pattern. In order to further identify the distribution of each supply and demand cluster in space, the clustering results are exported and connected with the 1 km grid ID used when extracting the supply and demand values, and the spatial pattern of the 5 important ecological product supply and demand relationship clusters in the lake ecological economic zone is shown in Figure 8 . The characteristics of the 10 clusters can be analyzed in combination with the supply and demand relationship characteristics and spatial distribution of each cluster.

[0035] Cluster 1: Carbon sequestration and food demand cluster. This cluster covers about 1.47% of the study area and is mainly distributed in the peripheral areas of urban built-up areas in various counties and cities. These areas are generally economic development zones and high-tech industrial parks away from central cities, and the land use type is mainly industrial and mining land, with a relatively high degree of population aggregation. The industrial production activities in these areas have a high demand for water, energy, and food, and the supply capacity within the cluster cannot maintain a balance between supply and demand, so carbon sequestration demand, food demand, and water production demand are the dominant characteristics, and a large amount of external service inflow is needed to support their social and economic activities.

[0036] 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. The cluster has low human activity intensity, large water evaporation, low terrain, and surrounding lowlands unsuitable for crop planting, so the supply and demand of various ecological functions in cluster 2 are at a low level.

[0037] Cluster 3: water production and purification synergistic supply cluster. Cluster 3 covers 7.72% of the area of the study area, with wetlands around surface water bodies as the main ecosystem type. The land use type is mainly paddy field, so cluster 3 retains a certain food supply capacity. The wetlands in the study area are mainly herbaceous marshes, with high water production and water purification capacity. The water production and purification services in the cluster mainly flow into surface water bodies to exert their functions.

[0038] Cluster 4: grain production function dominant-purification water supply cluster. Cluster 4 is the largest cluster, accounting for 29.83% of the total area. Spatially, cluster 4 occupies most of the central and northern plains of the study area, with cultivated land as the main land use type, providing important grain production functions for the local area. In addition, crop growth provides certain carbon sequestration functions. Large areas of contiguous paddy fields ensure its water production capacity and certain water purification capacity. However, the soil in the plain area is mainly paddy soil, and nitrogen, phosphorus, and potassium fertilizers are mainly used for agricultural planting, combined with the relatively soft soil structure, the loss of nitrogen, phosphorus, and other nutrients is serious under the erosion of rainfall, and the demand for water purification services is also high. Therefore, cluster 4 is also one of the source areas that need to be focused on for controlling non-point source pollution.

[0039] Cluster 5: grain carbon sequestration core demand cluster. Cluster 5 accounts for only 0.3% of the total area of the study area. Spatially, cluster 5 is mainly distributed in the central urban areas of each county and city in the form of points. This cluster is a typical high demand-low supply area with a huge demand for grain supply and carbon sequestration services. Its public infrastructure is relatively complete, the population is highly concentrated, and social and economic activities are intense, mostly in the core development areas of each town, but it also faces problems such as fragile ecological environment and serious pollution. With the increase in land development intensity and the continuous expansion of construction land, the ecological product supply-demand contradiction in cluster 5 will further intensify.

[0040] Cluster 6: Purification demand dominant - water production and grain supply cluster. Cluster 6 accounts for about 17.36% of the total area of the study area, mainly in the form of patches concentrated on both sides of a lake plain and hilly land in the south, with a small amount of strip-shaped distribution of cluster 6 in the valleys of the southeast. In terms of functional composition, the water quality purification demand is the most prominent in the cluster, and its grain supply, water production, and purification service functions are also at a medium to high level. The cluster is mainly hilly terrain, with farmland as the main land use. The vegetation cover is mainly crops, and the soil nutrients in these areas are lost with surface water due to the terrain and relatively simple ecological structure. Cluster 6 should be divided into a key prevention area for soil and water conservation, and comprehensive management measures should be implemented for land cover with high soil erosion risk such as slope farmland and erosion gully to improve the risk resistance of the local ecosystem.

[0041] Cluster 7: Carbon sequestration dominant - soil and water conservation supply cluster. Cluster 7 accounts for about 13.19% of the total area of the study area, mainly occupying the mountainous areas on both sides of the southwest and southeast of the study area. There is also a small amount of distribution in the northwest and east mountains. Cluster 7 is mainly located on the slopes of the waist of the mountains and in the valley areas, near water systems, with good ecological environment. Its land cover is mainly dense evergreen broad-leaved forest, with high vegetation cover, so carbon sequestration supply is the dominant function of this area. In terms of ecological function importance, cluster 7 mainly covers most of the important ecological space and a small amount of core area.

[0042] Cluster 8: Carbon sequestration and soil conservation supply cluster. Cluster 8 accounts for 15.57% of the total area of the study area, with a large area distributed on the slopes to the ridges of the mountains in the southwest and northwest, and on the top of the mountains in the southeast. In terms of ecological function, cluster 8 is far from human activity intrusion, with thick ecological background, dense vegetation cover, and rich ecological niche. Therefore, carbon sequestration function and soil conservation function are the most prominent value supply of cluster 8. Cluster 8 covers most of the important ecological protection core area, so the most strict ecological protection is needed for this area, and no commercial production and development activities are allowed in the forest area. The originality and integrity of the natural landscape, biodiversity, and ecological processes in the region should be maintained without human interference.

[0043] Cluster 9: Soil erosion demand cluster. Cluster 9 accounts for 3.74% of the total area, mainly distributed in hilly land areas and within the Danxia Landform National Geological Park. These areas are mostly located around the built-up areas of mountainous towns or have special hilly Danxia landforms. They are characterized by low vegetation cover and large terrain undulations. Although there is some carbon sequestration capacity, the risk of soil erosion is often very serious, and the demand for water quality purification and soil conservation is the most prominent ecological instability characteristic of these areas.

[0044] 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 of the eastern part of the study area. The ecological function characteristics of cluster 10 are basically similar to those of cluster 1, but mainly distributed in the central urban area where social and economic activities are frequent.

[0045] S3, ecological product value realization zoning and zoning implementation strategy making.

[0046] S31, according to the identification results of the important ecological product supply and demand relationship characteristic cluster, merging the ecological function clusters with similar functions and adjacent spatial positions, and superimposing the identification results of the important ecological space, obtaining the ecological product value realization zoning results and proposing the zoning implementation strategy and path.

[0047] From the township scale, based on the identification results of the important ecological product supply and demand relationship cluster, the ecological function clusters with similar functions and adjacent spatial positions are merged, and the ecological product value realization zoning of a certain lake ecological economic zone is carried out. Based on the characteristics of the ecological product supply and demand relationship cluster at the township scale, the dominant ecological function categories in each township unit are analyzed, the land use and the supply and demand relationship similar supply and demand clustering clusters are merged, and the identification results of the important ecological space are superimposed, and finally the ecological product value realization zoning results at the township scale are obtained as shown in Table 2.

[0048] Table 2: Ecological product value realization zoning results of a certain lake ecological economic zone at township scale ;

[0049] Example two The application also provides a natural resource ecological product value realization zoning system, comprising: An important ecological space identification module: used for performing ecological space zoning priority identification based on a Zonation model, priority identification result analysis, and important ecological space hierarchical threshold division and important ecological space identification result extraction based on STARS mutation test; An important ecological product supply and demand relationship cluster identification module: used for performing supply and demand relationship cluster identification method based on a SOFM algorithm and identification result analysis; An ecological product value realization zoning and zoning implementation strategy making module: used for making ecological product value realization zoning and zoning implementation strategy and path.

[0050] It is worth noting that the contents not elaborated in the application are all prior art and are well known to those skilled in the art.

[0051] Therefore, the application provides a natural resource ecological product value realization zoning method and system, important ecological space is accurately identified by coupling a Zonation model and a STARS algorithm, a SOFM algorithm is used to analyze ecological product supply and demand relationship clusters, ecological product value realization zoning and zoning implementation strategy formulation are performed, the problems of strong subjectivity and ignoring spatial structure of an existing zoning method are solved, and ecological resource optimal allocation and protection, accurate formulation of management strategies and regional green collaborative development are realized.

[0052] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit them, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or equivalently replace the technical solutions of the present application, and these modifications or equivalent replacements also cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

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: Iteratively sort the ecological function priorities at the landscape scale of the study area based on the spatial layout optimization zonation model to generate a landscape nested sequence ranked by ecological product value; 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 to determine the classification threshold of the important ecological space through mutation test, and divide the important ecological space; Step S4: Based on the results of the STARS mutation test analysis, the top 37% and 21% areas in the ecological function priority ranking are extracted as the general area and core area of ​​the important ecological space, respectively, and the areas are superimposed and compared with the existing ecological protection red line; Step S5: Using the self-organizing feature map network algorithm SOFM to perform cluster analysis on the supply and demand variables of important ecological products, and identify characteristic clusters of the supply and demand relationship of important ecological products; Step S6: Based on the identification results of the characteristic clusters of the supply and demand relationships of important ecological products, merge the ecological function clusters with similar functions and spatial proximity, and superimpose the important ecological space identification results to obtain the ecological product value realization zoning results and propose zoning realization strategies and paths.

2. A 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 at the landscape scale of the study area are iteratively ranked based on the zonation model. The specific steps include: Step S11: Set the number of iterations , calculate the marginal loss of all grids ; Step S12: Assigning a specific order to the grid creates a starting point for priority sorting and begins iterative sorting, where is the grid marginal loss of the original data; Step S13: Sorting vector constructed according to step S12 , move from low priority to high priority grid cells, and calculate the marginal loss of grid cells during the movement again , and reorder the grid cells according to the updated marginal loss ; The marginal loss algorithm adopts the core area removal method, and the formula is as follows: ； in, Grid cells Features The proportion of attribute residual to the original distribution of features, Features The weight of is the feature coverage strength, take =2, z is the number of characteristic attributes; Step S14: Check convergence. When the marginal loss increases steadily as the priority ranking moves, take the result of the last round of iteration as the final priority ranking.

3. A zoning method for realizing the value of natural resource ecological products according to claim 1, characterized in that: In step S3, the t-Test-based sequential algorithm STARS is based on a pre-set confidence level. and cut length Determine the mutation point when the state deviation index The credibility of the mutation identification result is the highest when it reaches the maximum, and the calculation formula is as follows: ； ； in, is the difference between the mean values ​​of the two groups of data. Is the confidence level The following has degrees of freedom k distribution value; For variables The mean square error of To cut to length; is the state offset index; For the samples; For variables The average value of is the sample size, Indicates that the confidence level is The mutation point determined at the level, q is the number of observations.

4. A 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 a random value in the range [0,1] to the connection weight from the input neuron to the output neuron. , determine the learning rate Initial value of ; Set the field , the maximum number of iterations is ; Step S52: Input data vector ,in, For the neurons; Step S53: Calculate the Euclidean distance between the input vector and the mapping layer weight vector , the formula is as follows: ； in, The number of training iterations is The first input neurons, The number of training iterations is The first The input neuron The connection weights between the input neurons, is the number of neurons; Step S54: Find the neuron closest to the mapping layer weight vector as the winning neuron, and correct all connection weights in the area. The formula is as follows: ； ； in, For the winning neuron, Features The weight of is the number of neurons in the mapping layer, is the weight vector change; Step S55: Update learning rate and fields , the formula is as follows: ； ； in, The number of training iterations is The learning rate when The number of training iterations is The field of time, Indicates rounding down; Step S56: Input new data vector , repeat steps S53-S55 until .

5. A zoning system for realizing the value of natural resource ecological products, characterized by: include: Important ecological space identification module: used to perform ecological space demarcation priority identification based on the zonation model, analyze priority identification results, determine the classification threshold of important ecological spaces based on the STARS mutation test, and extract important ecological space identification results; Important ecological product supply and demand relationship cluster identification module: used to implement the supply and demand relationship cluster identification method based on the 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 strategy.

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