An ecological zoning method based on ecosystem service drivers

Abstract

This invention discloses an ecological zoning method based on ecosystem service drivers, including: 1) determining the study area, timeframe, and primary ecosystem services; 2) establishing and preprocessing a research database to quantify the value of ecosystem services over multiple years; 3) calculating the correlation between ecosystem services and drivers across different years using a modified Pearson correlation coefficient method to identify the primary drivers; 4) using the response of ecosystem services to the primary drivers to evaluate and calculate the multi-year composite value of different ecosystem service capacities using a weighted approach; 5) using a clustering method to spatially cluster the primary drivers to identify regions with similar characteristics; and 6) integrating and optimizing fragmented patches to obtain the final ecological zoning. This invention precisely delineates ecosystem services from the perspective of the ecosystem service driver-response mechanism, providing a distinctive and scientifically credible basis for regional development and playing a role in coordinating regional trade-offs in development.

Description

Technical Field

[0001] The present invention relates to the field of ecological protection technology, and in particular to an ecological zoning method based on ecosystem service driving factors. Background Art

[0002] Ecosystem services refer to the functions and services provided by natural ecosystems in sustaining life, providing materials and energy, and regulating the environment. With the increase in human activity, ecosystem functions and services are facing increasing pressure. Scientifically and rationally demarcating ecological regions and optimizing ecological protection and resource utilization have become key issues in current ecological and environmental management. Spatial zoning of ecosystem services, particularly based on the drivers of these services, is an innovative approach to gain a deeper understanding and systematic integration of the distribution and dynamics of ecosystem services.

[0003] Traditional ecological zoning methods primarily rely on ecological types, geographic boundaries, or administrative divisions, rarely considering the drivers of ecosystem services, such as climate, land use patterns, and topographical characteristics. This single-factor approach fails to fully reflect the spatial variation and dynamics of ecosystem services and rarely considers the lagged effects of these drivers. Consequently, zoning methods tailored to the drivers of ecosystem services have emerged. These methods can better reveal the spatiotemporal variations in ecosystem services, more accurately capture the response mechanisms and evolution of ecosystems, and explain how time lags can be integrated with drivers. This allows for the optimization of ecological zoning methods and provides a scientific theoretical basis and practical guidance for ecological protection and resource utilization. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing an ecological zoning method based on ecosystem service drivers. This method addresses the arbitrary and subjective nature of zoning and the lack of scientific evaluation criteria found in traditional methods. This method aims to improve understanding of the spatial heterogeneity of ecosystem services and coordinate regional trade-offs in development. To achieve this objective, the present invention employs the following technical solutions.

[0005] An ecological zoning method based on ecosystem service drivers includes the following steps:

[0006] Step S1: Determine the study area, time frame, and main ecosystem services;

[0007] Step S2: Based on the characteristics of the study area and ecosystem services, obtain multi-source data, including data required for quantifying ecosystem services and data on corresponding driving factors of ecosystem services, establish a research database, pre-process the multi-source data, and quantify the value of ecosystem services over many years based on the model;

[0008] Step S3: Calculate the correlation between ecosystem services and driving factors in different years using the improved Pearson correlation coefficient to obtain the main driving factors with temporal and spatial correlations;

[0009] Step S4: Using the responses of ecosystem services to driving factors, a weighted approach is used to assess the multi-year integrated values ​​of different ecosystem service capacities;

[0010] Step S5: clustering the multi-year comprehensive values ​​of ecosystem service capacity to obtain spatial clustering of the main driving factors and analyze regions with similar characteristics of driving factors;

[0011] Step S6: Integrate and optimize the broken patches, integrate the broken patches in the spatial partition into other areas, and obtain the final ecological partition.

[0012] Specifically, the preprocessing of multi-source data in step S2 includes converting data from different sources into a unified format, interpolating data, and standardizing data to ensure comparability and consistency between different data sets, including:

[0013] First, we build a research database for computing ecosystem services by combining the ecosystem services of the studied area and the corresponding computational models.

[0014] Secondly, we preprocessed the driver data, using the variance inflation factor (VIF) to test for multicollinearity, and verified the relevance of the driver data using the KMO measure and Bartlett's sphericity test, to establish a driver research database corresponding to ecosystem services.

[0015] Subsequently, the multi-source data in the research database were converted to a unified format. The vector data and raster data in different projection coordinate systems were converted to a unified spatial reference system using the geographic information system software ArcGIS. The data types were standardized, the naming rules of the attribute table fields were standardized, and coding conflicts were resolved.

[0016] Finally, interpolation processing was performed using the ArcGIS Spatial Analyst spatial analysis tool in the ArcGIS software. For continuous variables, spatial interpolation methods such as IDW, Kriging, or spline interpolation were used to fill missing values, while for discrete variables, the nearest neighbor interpolation method was used. At the same time, bilinear interpolation was used to resample the multi-source data to the target resolution.

[0017] Specifically, in step S3, the correlation between ecosystem services and driving factors in different years is calculated using the improved Pearson correlation coefficient to obtain the main driving factors with temporal and spatial correlation. The process is as follows:

[0018] The main driving factors usually affect ecosystem service functions at different temporal and spatial scales, thereby changing the effectiveness and spatial distribution of ecosystem services. To obtain the nonlinear relationship and interaction effect between ecosystem services and driving factors, the correlation between ecosystem services and driving factors in different years was calculated using the improved Pearson correlation coefficient. The mathematical expression of the improved Pearson correlation coefficient model is:

[0019] ;

[0020] In the above formula, i Indicates the i Year; It is i Ecosystem services per year j and driving factors b The correlation coefficient of F tjb For the driving factors b Influence of the next t Ecosystem services per year j The spatial heterogeneity weight of Driving factors The average value of For the i Ecosystem services per year j The value of Serving the ecosystem j The multi-year average of

[0021] Screening the main driving factors. Based on the time lag effect of the driving factors on ecosystem services, the correlation coefficient is reflected and the correlation of each driving factor in each year is converted into a weight. The higher the correlation, the greater the weight. Based on the weight, by setting a threshold, screening is carried out to obtain the main driving factors with temporal and spatial correlation. The weight calculation process is as follows:

[0022] ;

[0023] In the above formula, It is i Mid-year ecosystem services j The weight of the correlation with driver b; T The accounting year.

[0024] Specifically, in step S4, the responses of ecosystem services to driving factors are used to evaluate the multi-year comprehensive values ​​of different ecosystem service capacities using a weighted approach, including the following:

[0025] First, calculate the weight of ecosystem services in each year:

[0026] ;

[0027] In the above formula, It is i Mid-year ecosystem services j The weight of B is the number of main driving factors with spatiotemporal correlations screened in step S3; J is the quantity of ecosystem services;

[0028] Secondly, calculate the comprehensive value of different ecosystem service capabilities over many years:

[0029] ;

[0030] In the above formula, It is the ecosystem service of different spatial data points j The comprehensive value of ecosystem service capacity; For the i Ecosystem services per year j value.

[0031] Furthermore, in step S5, the spatial clustering of the main driving factors is obtained by a clustering method. The steps of the clustering method are as follows:

[0032] Step S51: constructing the response points of the comprehensive ecosystem service value of each data point to the driving factors;

[0033] ;

[0034] In the above formula, It is a ecosystem services per data point j and driving factors F b the relationship between; It is a Data Point Ecosystem Services j The comprehensive value of f is a function that represents the response of ecosystem services to drivers;

[0035] Step S52: selecting a central cluster;

[0036] Based on the magnitude of the response of the driving factors to ecosystem services, the main driving factors of each ecosystem service were selected, and representative central clusters were selected based on the magnitude of the correlation. Based on the improved central cluster selection method, the response of ecosystem services to the driving factors was obtained. The calculation formula is as follows:

[0037] ;

[0038] ;

[0039] ;

[0040] In the above formula, It is a Data points and central clusters The weighted Euclidean distance between ; A is the total number of data points; It is a ecosystem services per data point j and driving factors F b the relationship between; It is a The spatial position of a point; It is k The center of the cluster; It is a The Euclidean distance between the data point and the center cluster is, according to the new cluster assignment, the updated cluster center is ;

[0041] Step S53: Repeat step S52 until the clustering result no longer changes, that is, the cluster assignment of each point does not change, or the set maximum number of iterations is reached; each cluster obtained represents a clustering area of ​​a type of driving factor, and the spatial points in these areas have similar driving factor characteristics in terms of ecosystem service response.

[0042] Specifically, in step S6, the broken patches are integrated and optimized, and the broken patches in the spatial partition are integrated into a large area with the same clustering characteristics to obtain the final ecological partition; the area of ​​the broken patches is less than 0.1% of the total area of ​​the study area.

[0043] Furthermore, in step S6, when there are differences in clustering characteristics between adjacent partitions during the process of integrating and optimizing fragmented patches, the spatial heterogeneity weight differences of ecosystem services between adjacent partitions are calculated. , further integrating the weight vector difference The broken patches are smoothed to reduce the broken patches and improve the rationality of the final ecological zoning.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The method of the present invention delineates ecological zones based on the driving characteristics of ecosystem services, which can better reveal the temporal and spatial variation patterns of ecological service functions and the time-delayed effects of driving factors (such as climate change and land use change) on ecosystem services. It provides a scientific theoretical basis and practical guidance for ecological protection and resource utilization, and solves the problems of arbitrary and subjective zoning and lack of scientific evaluation standards in traditional methods. It has the effect of improving the level of understanding of the spatial heterogeneity of ecosystem services and coordinating regional trade-offs in development.

[0046] 2. The method of the present invention accurately delineates ecosystem services from the perspective of ecosystem service-driven response mechanisms, providing a distinctive, scientific, and credible basis for regional development and possessing broad application potential and practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of an ecological zoning method based on ecosystem service driving factors of the present invention. DETAILED DESCRIPTION

[0048] In order to facilitate those skilled in the art to understand and implement the present invention, each step of the method proposed in the present invention is described in detail below. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the claims appended hereto.

[0049] Example

[0050] like Figure 1 As shown, the present invention discloses an ecological zoning method based on ecosystem service driving factors, comprising the following steps:

[0051] Step S1: Determine the study area, time frame, and main ecosystem services;

[0052] Step S2: Based on the characteristics of the study area and ecosystem services, obtain multi-source data, including data required for quantifying ecosystem services and data on corresponding driving factors of ecosystem services, establish a research database, pre-process the multi-source data, and quantify the value of ecosystem services over many years based on the model;

[0053] The research database includes data needed to quantify ecosystem services and the drivers of these services. These include temperature, precipitation, evapotranspiration, normalized difference vegetation index (NDVI), digital elevation model (DEM), land use and land cover change, net primary productivity, nighttime light, GDP per unit area, population per unit area, population change, GDP change, sand fraction, silt fraction, clay fraction, and soil organic matter content. The driver data should be processed to a grid size appropriate to the ecosystem service capacity; in this example, a 5km grid is used as the minimum pixel. Finally, based on the ecosystem service accounting model, the value of ecosystem services is quantified.

[0054] Step S3: Calculate the correlation between ecosystem services and driving factors in different years using the improved Pearson correlation coefficient to obtain the main driving factors with temporal and spatial correlations;

[0055] Step S4: Using the responses of ecosystem services to driving factors, a weighted approach is used to assess the multi-year integrated values ​​of different ecosystem service capacities;

[0056] Step S5: clustering the multi-year comprehensive values ​​of ecosystem service capacity to obtain spatial clustering of the main driving factors and analyze regions with similar characteristics of driving factors;

[0057] Step S6: Integrate and optimize the broken patches, integrate the broken patches in the spatial partition into other areas, and obtain the final ecological partition.

[0058] Specifically, the preprocessing of multi-source data in step S2 includes converting data from different sources into a unified format, interpolating data, and standardizing data to ensure comparability and consistency between different data sets, including:

[0059] First, we build a research database for computing ecosystem services by combining the ecosystem services of the studied area and the corresponding computational models.

[0060] Secondly, we preprocessed the driver data, using the variance inflation factor (VIF) to test for multicollinearity, and verified the relevance of the driver data using the KMO measure and Bartlett's sphericity test, to establish a driver research database corresponding to ecosystem services.

[0061] Subsequently, the multi-source data in the research database were converted to a unified format. The vector data and raster data in different projection coordinate systems were converted to a unified spatial reference system using the geographic information system software ArcGIS. The data types were standardized, the naming rules of the attribute table fields were standardized, and coding conflicts were resolved.

[0062] Finally, interpolation processing was performed using the ArcGIS Spatial Analyst spatial analysis tool in the ArcGIS software. For continuous variables, spatial interpolation methods such as IDW, Kriging, or spline interpolation were used to fill missing values, while for discrete variables, the nearest neighbor interpolation method was used. At the same time, bilinear interpolation was used to resample the multi-source data to the target resolution.

[0063] Furthermore, in step S2, the value of the ecosystem service is quantified based on the model over many years. In this embodiment, soil conservation is used as an example of the ecosystem service studied. The theoretical value of the soil conservation service capacity is The calculation is performed using the RUSLE model in InVEST, and the calculation formula is:

[0064] ;

[0065] In the above formula, For pixels i Theoretical value of annual soil conservation service capacity, unit ; is the rainfall erosivity factor, dimensionless; It is a pixel i Soil erosion coefficient on , dimensionless; For pixels i The slope length factor on , dimensionless; It is a pixel i Slope factor on , dimensionless; It is a pixel i Vegetation cover factor on , dimensionless; is the factor for conservation practices of land use, dimensionless.

[0066] Specifically, in step S3, the correlation between ecosystem services and driving factors in different years is calculated using the improved Pearson correlation coefficient to obtain the main driving factors with temporal and spatial correlation. The process is as follows:

[0067] The main driving factors usually affect ecosystem service functions at different temporal and spatial scales, thereby changing the effectiveness and spatial distribution of ecosystem services. To obtain the nonlinear relationship and interaction effect between ecosystem services and driving factors, the correlation between ecosystem services and driving factors in different years was calculated using the improved Pearson correlation coefficient. The mathematical expression of the improved Pearson correlation coefficient model is:

[0068] ;

[0069] In the above formula, i Indicates the i Year; It is i Ecosystem services per yearj and driving factors b The correlation coefficient of F tjb For the driving factors b Influence of the next t Ecosystem services per year j The spatial heterogeneity weight of Driving factors The average value of For the i Ecosystem services per year j The value of Serving the ecosystem j The multi-year average of

[0070] Screening the main driving factors. Based on the time lag effect of the driving factors on ecosystem services, the correlation coefficient is reflected and the correlation of each driving factor in each year is converted into a weight. The higher the correlation, the greater the weight. Based on the weight, by setting a threshold, screening is carried out to obtain the main driving factors with temporal and spatial correlation. The weight calculation process is as follows:

[0071] ;

[0072] In the above formula, It is i Mid-year ecosystem services j The weight of the correlation with driver b; T The accounting year.

[0073] Specifically, in step S4, the responses of ecosystem services to driving factors are used to evaluate the multi-year comprehensive values ​​of different ecosystem service capacities using a weighted approach, including the following:

[0074] First, calculate the weight of ecosystem services in each year:

[0075] ;

[0076] In the above formula, It is i Mid-year ecosystem services j The weight of B is the number of main driving factors with spatiotemporal correlations screened in step S3; J is the quantity of ecosystem services;

[0077] Secondly, calculate the comprehensive value of different ecosystem service capabilities over many years:

[0078] ;

[0079] In the above formula, It is the ecosystem service of different spatial data pointsj The comprehensive value of ecosystem service capacity; For the i Ecosystem services per year j value.

[0080] Furthermore, in step S5, the spatial clustering of the main driving factors is obtained by a clustering method. The steps of the clustering method are as follows:

[0081] Step S51: constructing the response points of the comprehensive ecosystem service value of each data point to the driving factors;

[0082] ;

[0083] In the above formula, It is a ecosystem services per data point j and driving factors F b the relationship between; It is a Data Point Ecosystem Services j The comprehensive value of f is a function that represents the response of ecosystem services to drivers;

[0084] Step S52: selecting a central cluster;

[0085] Based on the magnitude of the response of the driving factors to ecosystem services, the main driving factors of each ecosystem service were selected, and representative central clusters were selected based on the magnitude of the correlation. Based on the improved central cluster selection method, the response of ecosystem services to the driving factors was obtained. The calculation formula is as follows:

[0086] ;

[0087] ;

[0088] ;

[0089] In the above formula, It is a Data points and central clusters The weighted Euclidean distance between ; A is the total number of data points; It is a ecosystem services per data point j and driving factors F b the relationship between; It is a The spatial position of a point; It is k The center of the cluster; It is a The Euclidean distance between the data point and the center cluster is, according to the new cluster assignment, the updated cluster center is ;

[0090] Step S53: Repeat step S52 until the clustering result no longer changes, that is, the cluster assignment of each point does not change, or the set maximum number of iterations is reached; each cluster obtained represents a clustering area of ​​a type of driving factor, and the spatial points in these areas have similar driving factor characteristics in terms of ecosystem service response.

[0091] Specifically, in step S6, the broken patches are integrated and optimized, and the broken patches in the spatial partition are integrated into a large area with the same clustering characteristics to obtain the final ecological partition; the area of ​​the broken patches is less than 0.1% of the total area of ​​the study area.

[0092] Furthermore, in step S6, when there are differences in clustering characteristics between adjacent partitions during the process of integrating and optimizing fragmented patches, the spatial heterogeneity weight differences of ecosystem services between adjacent partitions are calculated. , further integrating the weight vector difference The broken patches are smoothed to reduce the broken patches and improve the rationality of the final ecological zoning.

[0093] In summary, the ecological zones divided by the method of the present invention have smooth and clear boundaries and few broken patches, which can better reveal the temporal and spatial variation patterns of ecological service functions and the time delay effect of the time lag of driving factors (such as climate change and land use change) on ecosystem services. The results of the ecological zone division using the method of the present invention can provide a scientific theoretical basis and practical guidance for ecological protection and resource utilization, solve the problems of arbitrary and subjective zoning and lack of scientific evaluation standards in traditional methods, and play a role in improving the level of understanding of the spatial heterogeneity of ecosystem services and coordinating regional trade-offs in development.

[0094] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

Claims

1. An ecological zoning method based on ecosystem service drivers, characterized by: The following steps are involved: Step S1: Determine the study area, time frame, and main ecosystem services; Step S2: Based on the characteristics of the study area and ecosystem services, obtain multi-source data, including data required for quantifying ecosystem services and data on corresponding driving factors of ecosystem services, establish a research database, pre-process the multi-source data, and quantify the value of ecosystem services over many years based on the model; Step S3: Calculate the correlation between ecosystem services and driving factors in different years using the improved Pearson correlation coefficient to obtain the main driving factors with temporal and spatial correlations; Major drivers typically affect ecosystem service functions at different temporal and spatial scales, thereby altering ecosystem service effectiveness and spatial distribution. To capture the nonlinear relationships and interactive effects between ecosystem services and drivers, the correlation between ecosystem services and drivers in different years was calculated using a modified Pearson correlation coefficient. The mathematical expression of the improved Pearson correlation coefficient model is: ； In the above formula, i Indicates the i Year; It is i Ecosystem services per year j and driving factors b The correlation coefficient of F tjb For the driving factors b Influence of the next t Ecosystem services per year j The spatial heterogeneity weight of Driving factors The average value of For the i Ecosystem services per year j The value of Serving the ecosystem j The multi-year average of Screening the main driving factors. Based on the time lag effect of the driving factors on ecosystem services, the correlation coefficient is reflected and the correlation of each driving factor in each year is converted into a weight. The higher the correlation, the greater the weight. Based on the weight, by setting a threshold, screening is carried out to obtain the main driving factors with temporal and spatial correlation. The weight calculation process is as follows: ； In the above formula, It is i Mid-year ecosystem services j The weight of the correlation with driver b; T The accounting year; Step S4: Using the responses of ecosystem services to driving factors, a weighted approach is used to assess the multi-year integrated values ​​of different ecosystem service capacities; First, calculate the weight of ecosystem services in each year: ； In the above formula, It is i Mid-year ecosystem services j The weight of B is the number of main driving factors with spatiotemporal correlations screened in step S3; J is the quantity of ecosystem services; Secondly, calculate the comprehensive value of different ecosystem service capabilities over many years: ； In the above formula, It is the ecosystem service of different spatial data points j The comprehensive value of ecosystem service capacity; For the i Ecosystem services per year j The value of Step S5: clustering the multi-year comprehensive values ​​of ecosystem service capacity to obtain spatial clustering of the main driving factors and analyze regions with similar characteristics of driving factors; Step S6: Integrate and optimize the broken patches, integrate the broken patches in the spatial partition into other areas, and obtain the final ecological partition.

2. The ecological zoning method based on ecosystem service driving factors according to claim 1 is characterized in that: The preprocessing of multi-source data in step S2 includes converting data from different sources into a unified format, interpolating data, and standardizing data to ensure comparability and consistency between different data sets. Specifically, it includes: First, we build a research database for computing ecosystem services by combining the ecosystem services of the studied area and the corresponding computational models. Secondly, we preprocessed the driver data, using the variance inflation factor (VIF) to test for multicollinearity, and verified the relevance of the driver data using the KMO measure and Bartlett's sphericity test, to establish a driver research database corresponding to ecosystem services. Subsequently, the multi-source data in the research database were converted to a unified format. The vector data and raster data in different projection coordinate systems were converted to a unified spatial reference system using the geographic information system software ArcGIS. The data types were standardized, the naming rules of the attribute table fields were standardized, and coding conflicts were resolved. Finally, interpolation processing was performed using ArcGIS Spatial Analyst, a spatial analysis tool in ArcGIS software, to fill missing values ​​using spatial interpolation methods such as IDW, Kriging, or spline interpolation for continuous variables, and the nearest neighbor interpolation method for discrete variables. At the same time, bilinear interpolation was used to resample multi-source data to the target resolution.

3. The method for ecological zoning based on ecosystem service driving factors according to claim 1 is characterized in that: In step S5, the spatial clustering of the main driving factors is obtained by clustering method. The steps of the clustering method are as follows: Step S51: constructing the response points of the comprehensive ecosystem service value of each data point to the driving factors; ； In the above formula, It is a ecosystem services per data point j and driving factors F b the relationship between; It is a Data Point Ecosystem Services j The comprehensive value of f is a function that represents the response of ecosystem services to drivers; Step S52: selecting a central cluster; Based on the magnitude of the response of the driving factors to ecosystem services, the main driving factors of each ecosystem service were selected, and representative central clusters were selected based on the magnitude of the correlation. Based on the improved central cluster selection method, the response of ecosystem services to the driving factors was obtained. The calculation formula is as follows: ； ； ； In the above formula, It is a Data points and central clusters The weighted Euclidean distance between ; A is the total number of data points; It is a ecosystem services per data point j and driving factors F b the relationship between; It is a The spatial position of a point; It is k The center of the cluster; It is a The Euclidean distance between the data point and the center cluster is, according to the new cluster assignment, the updated cluster center is ; Step S53, repeating step S52 until the clustering result does not change, that is, the cluster assignment of each point does not change, or the set maximum number of iterations is reached; Each cluster obtained represents a clustering area of ​​a type of driving factor, and the spatial points within these areas have similar driving factor characteristics in terms of ecosystem service response.

4. The method for ecological zoning based on ecosystem service driving factors according to claim 1 is characterized in that: In step S6, the broken patches are integrated and optimized, and the broken patches in the spatial partition are integrated into large areas with the same clustering characteristics to obtain the final ecological partition; the area of ​​the broken patches is less than 0.1% of the total area of ​​the study area.

5. The method for ecological zoning based on ecosystem service driving factors according to claim 4 is characterized in that: In the process of integrating and optimizing fragmented patches in step S6, when there are differences in clustering characteristics between adjacent partitions, the spatial heterogeneity weight differences of ecosystem services between adjacent partitions are calculated. , further integrating the weight vector difference The broken patches are smoothed to reduce the broken patches and improve the rationality of the final ecological zoning.

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