A preferred method for selecting a scale for ecosystem service research

Abstract

The application provides a preferred scale selection method for ecosystem service research, and relates to the technical field of ecosystem service evaluation. The method comprises the following steps: calculating the values of each ES index of each analysis unit under each scale according to the multi-scale division results and geographic data of a target region; constructing a spatial weight matrix according to the adjacent relationship of the analysis units under the same scale, and determining the Moran I index value of each ES according to the spatial weight matrix and the values of all ES indexes under the same scale, and then constructing a target decision matrix; determining the information entropy of each ES index according to the target decision matrix, and then determining the entropy weight weight of each ES index; constructing a weighted normalized decision matrix according to the target decision matrix and the determined entropy weight weight, and then determining the comprehensive distance between each scale and the ideal solution; determining the relative closeness of each scale to the ideal solution according to the comprehensive distance of each scale, and then determining the global optimal scale. The application aims to improve the selection accuracy of the universal scale.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecosystem service evaluation, in particular to a preferred scale selection method for ecosystem service research. BACKGROUND

[0002] In the field of ecosystem service (ES) evaluation and spatial planning, scientifically identifying the mechanism of multi-level influencing factors is the key basis for formulating sustainable decisions. As a core link, spatial scale selection directly determines the reliability of the analysis results. Different ES types (such as water conservation, biodiversity, etc.) have significant scale dependence. Small-scale analysis is easily disturbed by local noise, and large-scale analysis may mask spatial heterogeneity.

[0003] Current mainstream methods rely on expert experience to determine the scale or use single criteria such as landscape pattern index, ignoring the spatial autocorrelation effect and the synergistic optimization of multiple ES targets, resulting in strong subjectivity and low reproducibility of scale selection. Especially when dealing with ESs such as water and soil conservation and carbon storage, which have conflicting scale responses, traditional methods are difficult to quantify the differences in spatial aggregation intensity and cannot provide a universal scale framework for regional ecological management. SUMMARY

[0004] Therefore, the present application provides a preferred scale selection method for ecosystem service research. It aims to solve or partially solve the problems in the background art.

[0005] The present application provides a preferred scale selection method for ecosystem service research, which comprises:

[0006] Dividing the target region into multiple-scale spatial analysis units to obtain the division results of the target region corresponding to multiple spatial scales;

[0007] According to the division results and the obtained geographical data of the target region, the values of various ecosystem service indicators of each spatial analysis unit under each spatial scale are calculated;

[0008] According to the adjacent relationship of the spatial analysis units under the same spatial scale, a spatial weight matrix under the corresponding spatial scale is constructed, which records the adjacent relationship between each two spatial analysis units in the spatial weight matrix;

[0009] According to the spatial weight matrix under the same spatial scale and the values of all ecosystem service indicators, the Moran's I index value of each type of ecosystem service under the corresponding spatial scale is determined;

[0010] According to the Moran's I index values of various types of ecosystem services at all spatial scales, a target decision matrix is constructed, which records the research performance of various types of ecosystem services at various spatial scales;

[0011] According to the target decision matrix, information entropy of each type of ecosystem service index is determined;

[0012] According to the information entropy of each type of ecosystem service index, entropy weight of each type of ecosystem service index is determined;

[0013] According to the target decision matrix and the entropy weight of each type of ecosystem service index, a weighted normalized decision matrix is constructed;

[0014] According to the weighted normalized decision matrix, a comprehensive distance between each spatial scale and ideal solution is determined; wherein, the ideal solution includes a positive ideal solution and a negative ideal solution, the positive ideal solution is the maximum Moran's I value of each type of ecosystem service at each spatial scale, and the negative ideal solution is the minimum Moran's I value of each type of ecosystem service at each spatial scale;

[0015] According to the comprehensive distance of each spatial scale, a relative closeness between each spatial scale and ideal solution is determined;

[0016] According to the relative closeness of each spatial scale, a global optimal spatial scale is determined from all spatial scales.

[0017] The preferred scale selection method for ecosystem service research provided in the present application has the following advantages:

[0018] The preferred scale selection method for ecosystem service research provided in the application first divides the target region into multiple scale spatial analysis units to obtain the division results of multiple spatial scales corresponding to the target region; calculates the values of various ecosystem service indicators of each spatial analysis unit under each spatial scale according to the division results and the obtained geographic data of the target region; constructs a spatial weight matrix under the corresponding spatial scale according to the adjacent relationship of the spatial analysis units under the same spatial scale, and records the adjacent relationship between each two spatial analysis units in the spatial weight matrix; determines the Moran's I index value of each type of ecosystem service under the corresponding spatial scale according to the spatial weight matrix under the same spatial scale and the values of all ecosystem service indicators; constructs a target decision matrix according to the Moran's I index values of each type of ecosystem service under all spatial scales, and the target decision matrix records the research performance of each type of ecosystem service under various spatial scales; determines the information entropy of each type of ecosystem service indicator according to the target decision matrix; determines the entropy weight of each type of ecosystem service indicator according to the information entropy of each type of ecosystem service indicator; constructs a weighted normalized decision matrix according to the target decision matrix and the entropy weight of each type of ecosystem service indicator; determines the comprehensive distance between each spatial scale and the ideal solution respectively according to the weighted normalized decision matrix; wherein, the ideal solution includes a positive ideal solution and a negative ideal solution, the positive ideal solution is the maximum Moran's I value of each type of ecosystem service in each spatial scale, and the negative ideal solution is the minimum Moran's I value of each type of ecosystem service in each spatial scale; determines the relative closeness between each spatial scale and the ideal solution respectively according to the comprehensive distance of each spatial scale; determines the global optimal spatial scale from all spatial scales according to the relative closeness of each spatial scale.

[0019] The preferred scale selection method for ecosystem service research provided in the application dynamically quantifies the spatial variation characteristics of ecosystem services through the entropy weight method, and assigns differentiated weights to multiple types of ecosystem service indicators (the higher the spatial variation of an ecosystem service (such as biodiversity), the higher the weight, ensuring that key volatile elements play a core role in decision-making). On this basis, the spatial statistical distance between each scale scheme and the ideal solution is calculated based on the spatial aggregation intensity represented by Moran's I, and the global optimal scale scheme is finally output. This dual algorithm coordination mechanism effectively solves the typical scale conflict problem in ecosystem service evaluation (such as carbon storage which requires large scale to capture macro patterns, and biodiversity which requires small scale to analyze local details), and for the first time realizes universal scale decision-making under multiple objective constraints. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments of the present application will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 A flow chart of a preferred scale selection method for ecosystem service research according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be clearly and completely described in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0023] REFERENCE Figure 1 , Figure 1 A flow chart of a preferred scale selection method for ecosystem service research according to an embodiment of the present application. As shown in Figure 1 , the method comprises:

[0024] Step S01: performing multi-scale spatial analysis unit division on a target region to obtain a division result of the target region corresponding to multiple spatial scales.

[0025] In the embodiment, first, a target region to be researched is determined, and spatial analysis unit division is performed in the target region according to different spatial scales, so as to constitute a division result of multiple spatial scales in the target region. For example, the target region is divided into 1km×1km, 5km×5km, and 10km×10km grids, respectively, to obtain a spatial analysis unit division result under a 1km×1km spatial scale (i.e., the target region is divided into unit cells with a spatial scale of 1km×1km), a spatial analysis unit division result under a 5km×5km spatial scale, and a spatial analysis unit division result under a 10km×10km spatial scale.

[0026] Step S02: calculating the values of various ecosystem service indexes of each spatial analysis unit under each spatial scale according to the division result and obtained geographic data of the target region.

[0027] In the embodiment, the application also acquires geographical data in the target region, which at least includes land use / land cover data, digital elevation model (DEM), climate data and soil data. After the target region is divided into spatial analysis units of multiple spatial scales in step S01, the geographical data of the target region is used to calculate the values of various ecosystem service indicators for the spatial analysis unit division results of each spatial scale, that is, each spatial analysis unit of each spatial scale will calculate the values of various ecosystem service indicators unique to the spatial analysis unit. For example, a target region of 10 km x 10 km is divided into spatial analysis units of 1 km x 1 km, a total of 100 spatial analysis units, and spatial analysis units of 5 km x 5 km, a total of 4 spatial analysis units. Each spatial analysis unit of the 100 spatial analysis units of the spatial scale of 1 km x 1 km will calculate the value of each type of ecosystem service indicator, and each spatial analysis unit of the 4 spatial analysis units of the spatial scale of 5 km x 5 km will also calculate the value of each type of ecosystem service indicator. The application preferably calculates the values of the ecosystem service indicators by using the InVEST model.

[0028] In the embodiment, in an optional implementation, the various types of ecosystem service indicators calculated in the application include water conservation ecosystem service indicators, carbon storage ecosystem service indicators, soil conservation ecosystem service indicators, and nitrogen output ecosystem service indicators. It should be understood that in different analysis and research scenarios, the types of various ecosystem service indicators calculated are different. For example, if the ecosystem services to be studied in region A are a, b, c, and d, then when the preferred scale is determined by using the method of the application, the types of various ecosystem service indicators calculated are a, b, c, and d. If the ecosystem services to be studied in region A are a, b, and c, then when the preferred scale is determined by using the method of the application, the types of various ecosystem service indicators calculated are a, b, and c.

[0029] Step S03: According to the adjacent relationship of the spatial analysis units under the same spatial scale, a spatial weight matrix under the corresponding spatial scale is constructed, which records the adjacent relationship between each two spatial analysis units.

[0030] In the embodiment, the spatial weight matrix of the spatial analysis unit at one spatial scale is taken as an example to illustrate the construction of the spatial weight matrix of the spatial analysis unit at each spatial scale. The spatial weight matrix of the spatial analysis unit at the current spatial scale is constructed based on the adjacency relationship between the spatial analysis units at the current spatial scale according to the Queen adjacency criterion. One row of the constructed spatial weight matrix corresponds to one spatial analysis unit at the current spatial scale, and the number of rows is the same as the total number of the spatial analysis units at the current spatial scale. Meanwhile, one column of the constructed spatial weight matrix corresponds to one spatial analysis unit at the current spatial scale, and the number of columns is the same as the total number of the spatial analysis units at the current spatial scale. The value recorded in an element of the spatial weight matrix indicates whether the two spatial analysis units corresponding to the element are adjacent. For example, if the study area is one 9x9 grid, the spatial weight matrix at the current spatial scale is a 9x9 matrix. When the first row of the matrix corresponds to the spatial analysis unit a at the current spatial scale and the second column corresponds to the spatial analysis unit b at the current spatial scale, the value of the element at the intersection of the first row and the second column of the matrix indicates the adjacency relationship between the spatial analysis unit a and the spatial analysis unit b at the current spatial scale. For example, a value of 0 indicates that the two spatial analysis units are not adjacent, and a value other than 0 indicates that the two spatial analysis units are adjacent. Through the same implementation, the spatial weight matrix corresponding to each spatial scale can be determined.

[0031] Step S04: According to the spatial weight matrix at the same spatial scale and the values of all ecosystem service indicators, the Moran's I index value of each type of ecosystem service at the corresponding spatial scale is determined.

[0032] In the embodiment, after the values of each type of ecosystem service indicator of each spatial analysis unit at each spatial scale are calculated through step S02, and the spatial weight matrix at each spatial scale is calculated through step S03, the Moran's I index value of each type of ecosystem service at the same spatial scale is calculated based on the spatial weight matrix at the same spatial scale and the values of each type of ecosystem service indicator of each spatial analysis unit at the same spatial scale. Thus, one Moran's I index value corresponding to each type of ecosystem service at one spatial scale is calculated. For example, the types of ecosystem services include a, b, c, and d, and four Moran's I index values are calculated at one spatial scale, which correspond to the Moran's I index values of the four types of ecosystem services a, b, c, and d at the spatial scale, respectively. Through the same implementation, the Moran's I index value of each type of ecosystem service corresponding to each spatial scale is calculated.

[0033] Step S05: According to the Moran's I index value of each type of ecosystem service under all spatial scales, a target decision matrix is constructed, which records the research performance of each type of ecosystem service under various spatial scales.

[0034] In this embodiment, after the Moran's I index value of each type of ecosystem service under each spatial scale is calculated through step S04, a corresponding target decision matrix is constructed, which records the research performance of each type of ecosystem service under various spatial scales. The rows in the target decision matrix represent different spatial scales, the columns in the target decision matrix represent different ecosystem services, and an element in the target decision matrix represents the research performance of a certain type of ecosystem service under various spatial scales. For example, an element Xam in the target decision matrix points to the row corresponding to the spatial scale a, and the column corresponding to the ecosystem service m. The value of the element Xam represents the research performance of the ecosystem service m under the spatial scale a. The better the research performance represented by the value, the better the research of the ecosystem service m under the spatial scale a.

[0035] Step S06: According to the target decision matrix, the information entropy of each type of ecosystem service index is determined.

[0036] In this embodiment, after the corresponding target decision matrix is constructed through step S05, the information entropy of each type of ecosystem service index is determined based on the target decision matrix. When calculating the information entropy of a type of ecosystem service index, the research performance of the type of ecosystem service under each spatial scale recorded in the target decision matrix is determined. The information entropy of a type of ecosystem service index reflects the variability of the type of ecosystem service. The smaller the information entropy value of the ecosystem service index, the greater the variability of the Moran's I value of the type of ecosystem service between different spatial scales.

[0037] Step S07: According to the information entropy of each type of ecosystem service index, the entropy weight weight of each type of ecosystem service index is determined.

[0038] In this embodiment, after the information entropy of each type of ecosystem service index is calculated through step S06, the entropy weight weight of each type of ecosystem service index is determined based on the information entropy of all types of ecosystem service indexes obtained. When calculating the entropy weight weight of a type of ecosystem service index, the relationship between the information entropy of the type of ecosystem service index and the sum of the information entropy of all types of ecosystem services is determined. The greater the variability of the Moran's I value of a type of ecosystem service between different spatial scales, the smaller the corresponding information entropy. The smaller the information entropy of the ecosystem service index, the greater the entropy weight weight of the ecosystem service.

[0039] Step S08: constructing a weighted normalized decision matrix according to the target decision matrix and the entropy weight of each type of ecosystem service index.

[0040] In this embodiment, after obtaining the entropy weight of each type of ecosystem service index through step S07, a corresponding weighted normalized decision matrix is constructed based on the entropy weight of each type of ecosystem service index and the target decision matrix generated in step S05.

[0041] Step S09: determining the comprehensive distance between each spatial scale and ideal solution according to the weighted normalized decision matrix; wherein the ideal solution includes a positive ideal solution and a negative ideal solution, the positive ideal solution being the maximum Moran's I value of each type of ecosystem service in each spatial scale, and the negative ideal solution being the minimum Moran's I value of each type of ecosystem service in each spatial scale.

[0042] In this embodiment, the comprehensive distance between each spatial scale and ideal solution is calculated and determined based on the data recorded in the weighted normalized decision matrix constructed in step S08. Each spatial scale has a corresponding comprehensive distance. The ideal solution includes a positive ideal solution and a negative ideal solution. The positive ideal solution is the maximum Moran's I value of each type of ecosystem service in each spatial scale, i.e. the positive ideal solution corresponds to the ecosystem service, and there are as many positive ideal solutions as there are ecosystem services. For example, the ecosystem services include type a and type b, all spatial scales include scale 1 and scale 2, and the Moran's I value of the type a ecosystem service under scale 1 is the maximum, then the Moran's I value of the type a ecosystem service under scale 1 is the positive ideal solution for this type of ecosystem service, and the Moran's I value of the type b ecosystem service under scale 2 is the maximum, then the Moran's I value of the type b ecosystem service under scale 2 is the positive ideal solution for this type of ecosystem service. The negative ideal solution is the minimum Moran's I value of each type of ecosystem service in each spatial scale, i.e. the negative ideal solution corresponds to the ecosystem service, and there are as many negative ideal solutions as there are ecosystem services. For example, the ecosystem services include type c and type d, all spatial scales include scale 3 and scale 4, and the Moran's I value of the type c ecosystem service under scale 3 is the minimum, then the Moran's I value of the type c ecosystem service under scale 3 is the negative ideal solution for this type of ecosystem service, and the Moran's I value of the type d ecosystem service under scale 4 is the minimum, then the Moran's I value of the type d ecosystem service under scale 4 is the negative ideal solution for this type of ecosystem service.

[0043] Step S010: determining the relative closeness between each spatial scale and the ideal solution respectively according to the comprehensive distance of each spatial scale.

[0044] In this embodiment, after obtaining the comprehensive distance corresponding to each spatial scale through step S09, the relative closeness between each spatial scale and the ideal solution respectively is determined.

[0045] Step S011: determining the global optimal spatial scale from all spatial scales according to the relative closeness of each spatial scale.

[0046] In this embodiment, after obtaining the relative closeness of each spatial scale, all spatial scales are sorted in descending order of relative closeness, and the spatial scale with the highest relative closeness is determined as the global optimal spatial scale for ecological system service analysis of the target region.

[0047] The preferred scale selection method for ecological system service research provided in the application first divides the target region into multiple scale spatial analysis units to obtain the division results of multiple spatial scales corresponding to the target region; calculates the values of various ecological system service indicators of each spatial analysis unit under each spatial scale according to the division results and the obtained geographic data of the target region; constructs a spatial weight matrix under the corresponding spatial scale according to the adjacent relationship of the spatial analysis units under the same spatial scale, and records the adjacent relationship between each two spatial analysis units in the spatial weight matrix; determines the Moran's I index value of each type of ecological system service under the corresponding spatial scale according to the spatial weight matrix under the same spatial scale and the values of all ecological system service indicators; constructs a target decision matrix according to the Moran's I index values of all types of ecological system services under all spatial scales, and the target decision matrix records the research performance of each type of ecological system service under each spatial scale; determines the information entropy of each type of ecological system service indicator according to the target decision matrix; determines the entropy weight weight of each type of ecological system service indicator according to the information entropy of each type of ecological system service indicator; constructs a weighted normalized decision matrix according to the target decision matrix and the entropy weight weight of each type of ecological system service indicator; determines the comprehensive distance between each spatial scale and the ideal solution respectively according to the weighted normalized decision matrix; wherein, the ideal solution includes a positive ideal solution and a negative ideal solution, the positive ideal solution is the maximum Moran's I value of each type of ecological system service in each spatial scale, and the negative ideal solution is the minimum Moran's I value of each type of ecological system service in each spatial scale; determine the relative closeness between each spatial scale and the ideal solution respectively according to the comprehensive distance of each spatial scale; determine the global optimal spatial scale from all spatial scales according to the relative closeness of each spatial scale.

[0048] The preferred scale selection method for ecosystem service research provided in the application dynamically quantifies the spatial variation characteristics of ecosystem services by entropy weight method, and assigns differentiated weights to various ecosystem service indicators (the higher the spatial variation of an ecosystem service (such as biodiversity), the higher the weight, and the key fluctuation elements are ensured to play a core role in decision-making). On this basis, the spatial statistical distance between each scale scheme and the ideal solution is calculated based on the spatial aggregation intensity represented by Moran's I, and finally the globally optimal scale scheme is output. The dual algorithm cooperative mechanism effectively solves the typical scale conflict problem in ecosystem service evaluation (such as carbon storage which needs large scale to capture macro pattern, and biodiversity which needs small scale to analyze local details), and first realizes the universal scale decision under multi-objective constraints.

[0049] In combination with the above embodiments, in an implementation manner, the embodiments of the application further provide a preferred scale selection method for ecosystem service research. In the preferred scale selection method for ecosystem service research, step S03 can include steps S03_1 to S03_4:

[0050] Step S03_1: constructing a basic matrix corresponding to all spatial analysis units under the same spatial scale in rows and columns, wherein each row and each column respectively represents a spatial analysis unit.

[0051] In the embodiment, an optional implementation manner of constructing a spatial weight matrix corresponding to each spatial scale is as follows: first, a basic matrix is established, one row of the basic matrix corresponds to one spatial analysis unit under the current spatial scale, and the number of rows is the same as the total number of spatial analysis units under the current spatial scale. Meanwhile, one column of the basic matrix corresponds to one spatial analysis unit under the current spatial scale, and the number of columns is the same as the total number of spatial analysis units under the current spatial scale. For example, if the research area is a 9-grid, the basic matrix is a 9x9 matrix.

[0052] Step S03_2: determining whether the two spatial analysis units corresponding to each element in the basic matrix are adjacent.

[0053] In the embodiment, one element in the basic matrix corresponds to two spatial analysis units under the current spatial scale, and whether the two spatial analysis units corresponding to each element are adjacent is determined according to the actual division result. Specifically, the adjacent relationship is determined according to the Queen adjacency criterion, that is, as long as the two spatial analysis units under the current spatial scale share a boundary or a vertex, they are determined to be adjacent.

[0054] Step S03_3: in the case where the two spatial analysis units corresponding to the element are adjacent, recording the element as 1, and in the case where the two spatial analysis units corresponding to the element are not adjacent, recording the element as 0, to thereby construct a spatial adjacency matrix.

[0055] In the embodiment, if the two spatial analysis units corresponding to a single element in the base matrix at the current spatial scale are adjacent, the element is recorded as 1. If the two spatial analysis units corresponding to a single element in the base matrix at the current spatial scale are not adjacent, the element is recorded as 0. When the row and column corresponding to a single element in the base matrix point to the same spatial analysis unit, there is only one spatial analysis unit corresponding to the element, and the spatial analysis unit is not adjacent to itself, so such an element is recorded as 0.

[0056] Step S03_4: normalizing each element in each row of the spatial adjacency matrix to construct a spatial weight matrix, wherein the sum of the values in each row of the spatial weight matrix is 1.

[0057] In the embodiment, after the data of each element in the base matrix at the current spatial scale is recorded by step S03_3, the base matrix recording the adjacent information of the spatial analysis units is processed row by row. The processing method is to divide the value of an element in the same row by the sum of the values of all elements in the row to obtain a value that replaces the original value of the element. Through this processing method, the sum of all values in a row of the base matrix is 1, thereby obtaining the spatial weight matrix at the current spatial scale. For example, the second row of the matrix recording the adjacent information of the spatial analysis units records five 1s, and each 1 is processed to 0.2 so that the sum of the second row is 1. This way of constructing the spatial weight matrix ensures that the sum of the influence weights of each spatial analysis unit on its adjacent spatial analysis units is equal, eliminating the influence of the difference in the number of spatial analysis units (i.e., the number of grids at different spatial scales) or the number of adjacent spatial analysis units, so that the spatial effects at different spatial scales are comparable. Through the same implementation, a corresponding spatial weight matrix can be constructed for the division result of the spatial analysis units at each spatial scale.

[0058] In combination with the above embodiments, in an implementation, the embodiments of the present application also provide a preferred scale selection method for ecosystem service research. In the preferred scale selection method for ecosystem service research, step S04 can include: calculating the values of the spatial weight matrix at the same spatial scale and all ecosystem service indicators by a predefined Moran's I index algorithm to obtain the Moran's I index values of various types of ecosystem services at the corresponding spatial scale;

[0059] The expression of the Moran's I index algorithm is:

[0060]

[0061] wherein n is the number of spatial analysis units of the target region at the corresponding spatial scale; is the value of the element corresponding to the i-th spatial analysis unit and the j-th spatial analysis unit in the spatial weight matrix at the corresponding spatial scale; are the values of the corresponding category ecosystem service indicators of the i-th and j-th spatial analysis units at the corresponding spatial scale, respectively; is the mean value of the values of the corresponding category ecosystem service indicators of all spatial analysis units at the corresponding spatial scale; is the variance of the values of the corresponding category ecosystem service indicators of all spatial analysis units at the corresponding spatial scale.

[0062] In the present embodiment, the present application defines a Moran's I index algorithm in advance, and the Moran's I index of each category of ecosystem service at each spatial scale can be calculated by the Moran's I index algorithm. When calculating the Moran's I index of a category of ecosystem service at a spatial scale, the number of spatial analysis units at the spatial scale, the spatial weight matrix at the spatial scale, the values of the category of ecosystem service indicators (such as the values of water conservation ecosystem service indicators) of each spatial analysis unit at the spatial scale, and the variance of the values of the category of ecosystem service indicators of all spatial analysis units at the spatial scale are substituted into the Moran's I index algorithm for calculation, and the Moran's I index value of the category of ecosystem service at the spatial scale can be obtained. For example, in the case that the types of ecosystem services include water conservation, carbon storage, soil conservation and nitrogen output, four Moran's I index values of the four categories of ecosystem services at a spatial scale can be obtained. The higher the Moran's I index value is, the stronger the spatial aggregation degree of ecological function (such as high carbon area) is, and the more beneficial it is to analyze the ecosystem service law at the spatial scale. Since the present application calculates a corresponding Moran's I index value for each category of ecosystem service at a spatial scale, the present application evaluates the spatial scale for the Moran's I index values of multiple categories of ecosystem services at the same spatial scale.

[0063] In combination with the above embodiments, in an implementation, the present application further provides a preferred scale selection method for ecosystem service research. In the preferred scale selection method for ecosystem service research, step S05 can include steps S05_1 to S05_2:

[0064] ​Step S05_1: constructing a decision matrix between ecosystem services and spatial scales, wherein rows represent different spatial scales, and columns represent Moran's I index values of each type of ecosystem service.

[0065] In this embodiment, first, according to the Moran's I index values of each type of ecosystem service at all spatial scales obtained by calculation, a decision matrix between ecosystem services and spatial scales is constructed, wherein rows represent different spatial scales, and columns represent Moran's I index values of each type of ecosystem service.

[0066] Step S05_2: performing standardization processing on the decision matrix by a standardization algorithm to obtain a target decision matrix;

[0067] The expression of the standardization algorithm is:

[0068]

[0069] wherein, represents a matrix element in the target decision matrix corresponding to the jth type of ecosystem service under the ith spatial scale; is the Moran's I index value of the jth type of ecosystem service under the ith spatial scale in the decision matrix; and m is the total number of spatial scales.

[0070] In this embodiment, after the decision matrix is constructed, the decision matrix is calculated by a pre-defined standardization algorithm, and a corresponding target decision matrix is constructed based on all calculation results, which has the same size as the decision matrix, but the values of the matrix elements in the target decision matrix are replaced by the values of the matrix elements calculated by the standardization algorithm. The values of the matrix elements in the target decision matrix are replaced by the values of the matrix elements calculated by the standardization algorithm. The corresponding target decision matrix is constructed.

[0071] In combination with the above embodiments, in an implementation, the embodiments of the present application further provide a preferred scale selection method for ecosystem service research. In the preferred scale selection method for ecosystem service research, step S06 can include: calculating the target decision matrix by a pre-defined information entropy determination algorithm to obtain information entropy of each type of ecosystem service index.

[0072] The expression of the information entropy determination algorithm is:

[0073]

[0074] wherein, is the information entropy of the jth type of ecosystem service index; and k = 1 / ln(m) is an adjustment constant. represents a matrix element in the target decision matrix corresponding to the jth type of ecosystem service under the ith spatial scale; m is the total number of spatial scales.

[0075] In the embodiment, the information entropy determination algorithm is predefined, and the information entropy values of the ecosystem service indexes are calculated by substituting the data in the target decision matrix into the information entropy determination algorithm. The information entropy value of the ecosystem service index reflects the variation degree of the ecosystem service. The smaller the information entropy value of the ecosystem service index, the greater the variation degree of the Moran's I value of the ecosystem service between different spatial scales.

[0076] In combination with the above embodiment, in an implementation, the embodiment of the present application further provides a preferred scale selection method for ecosystem service research. In the preferred scale selection method for ecosystem service research, step S07 can include: calculating the information entropy of each type of ecosystem service index by using a predefined entropy weight determination algorithm to obtain the entropy weight of each type of ecosystem service index.

[0077] The expression of the entropy weight determination algorithm is:

[0078]

[0079] wherein, is the entropy weight of the jth type of ecosystem service index, is the information entropy of the jth type of ecosystem service index, and satisfies the constraint condition: .

[0080] In the embodiment, the entropy weight determination algorithm is predefined, and the entropy weight of each type of ecosystem service index is calculated by substituting the calculated information entropy of each type of ecosystem service index into the information entropy determination algorithm. The greater the variation degree of the Moran's I value of the ecosystem service between different spatial scales, the smaller the corresponding information entropy value. The smaller the corresponding information entropy value of the ecosystem service, the greater the entropy weight of the ecosystem service.

[0081] In combination with the above embodiment, in an implementation, the embodiment of the present application further provides a preferred scale selection method for ecosystem service research. In the preferred scale selection method for ecosystem service research, step S08 can include steps S08_1 to S08_3:

[0082] Step S08_1: sequentially obtaining a single element in the target decision matrix.

[0083] In the embodiment, a target element in the target decision matrix is multiplied by the entropy weight of the ecosystem service type of the column corresponding to the target element to obtain a corresponding weighted and normalized decision matrix, where the target element is any element in the target decision matrix. Specifically, an element in the target decision matrix is obtained each time, and the specific type of the ecosystem service corresponding to the element is determined.

[0084] Step S08_2: An entropy weight of the ecosystem service of the column corresponding to the single element is obtained, and the value of the single element is multiplied by the obtained entropy weight to obtain a corresponding target value.

[0085] In the embodiment, after an element in the target decision matrix is obtained and the specific type of the ecosystem service corresponding to the element is determined, the entropy weight of the type of the ecosystem service calculated in advance is obtained, and then the value of the element is multiplied by the entropy weight of the type of the ecosystem service to obtain a corresponding target value. The value of the element in the target decision matrix is replaced by the target value. Thus, through the same implementation, each element in the target decision matrix can obtain a corresponding target value.

[0086] Step S08_3: A weighted and normalized decision matrix is constructed according to all target values obtained from all elements in the target decision matrix.

[0087] In the embodiment, after each element in the target decision matrix obtains a corresponding target value, the original value of the element in the target decision matrix is replaced by the target value corresponding to the element itself to obtain a final weighted and normalized decision matrix. For example, the target decision matrix includes elements a1, a2, a3,..., a9, the value of the element an (n is 1, 2, 3,..., 9) in the target decision matrix is xn, and after the target value yn corresponding to the element an in the target decision matrix is calculated, the original xn value of the element an is replaced by the target value yn of the element an to obtain a weighted and normalized decision matrix composed of the target value y1 of the element a1, the target value y2 of the element a2, the target value y3 of the element a3,..., and the target value y9 of the element a9.

[0088] In combination with the above embodiments, in an implementation, the embodiments of the present application further provide a preferred scale selection method for ecosystem service research. In the preferred scale selection method for ecosystem service research, step S09 can include: calculating the weighted and normalized decision matrix by using a predefined comprehensive distance algorithm to obtain a comprehensive distance between each spatial scale and the ideal solution.

[0089] The expression of the comprehensive distance algorithm is:

[0090]

[0091]

[0092] wherein, is the positive distance between the i-th spatial scale and the positive ideal solution; is the negative distance between the i-th spatial scale and the negative ideal solution; is the matrix element in the weighted normalized decision matrix corresponding to the j-th ecosystem service under the i-th spatial scale, representing the performance value of the i-th spatial scale on the j-th ecosystem service index; is the positive ideal solution of the j-th ecosystem service, representing the maximum Moran's I value of the j-th ecosystem service in each spatial scale; is the negative ideal solution of the j-th ecosystem service, representing the minimum Moran's I value of the j-th ecosystem service in each spatial scale.

[0093] In the embodiment, the comprehensive distance algorithm is predefined, and the data in the constructed weighted normalized decision matrix, the maximum Moran's I value of each ecosystem service in all spatial scales, and the minimum Moran's I value of each ecosystem service in all spatial scales are substituted into the comprehensive distance algorithm for calculation, so as to obtain the positive distance between each spatial scale and the positive ideal solution, and the negative distance between each spatial scale and the negative ideal solution. This step aims to quantify the comprehensive performance of each spatial scale scheme under multi-dimensional standards, and the calculation method is to measure the Euclidean distance between the vector point representing each spatial scale scheme and the positive and negative ideal solution vector points. This calculation is not an independent deviation analysis for a single ecosystem service (ES) index, but a multi-dimensional spatial distance evaluation of all ES indexes as a whole.

[0094] In combination with the above embodiment, in an implementation manner, the embodiment also provides a preferred scale selection method for ecosystem service research. In the preferred scale selection method for ecosystem service research, step S010 can include: determining the relative closeness of each spatial scale to the ideal solution by using a predefined closeness algorithm; and the expression of the closeness algorithm is:

[0095]

[0096] wherein, is the relative closeness of the i-th spatial scale to the ideal solution, ; is the positive distance between the i-th spatial scale and the positive ideal solution, is the negative distance between the ith spatial scale and the negative ideal solution.

[0097] In the embodiment, the closeness degree algorithm is predefined, and the relative closeness degree corresponding to the spatial scale is obtained by substituting the positive distance between the spatial scale and the positive ideal solution and the negative distance between the spatial scale and the negative ideal solution into the closeness degree algorithm for calculation.

[0098] In combination with the above embodiments, in an implementation, the embodiments of the present application further provide a preferred scale selection method for ecosystem service research. In the preferred scale selection method for ecosystem service research, the method further comprises:

[0099] Step S012: determining the ecosystem service aggregation intensity of the global optimal spatial scale, and determining whether there is an abnormal Moran's I index value under the global optimal spatial scale.

[0100] In the embodiment, the ecosystem service aggregation intensity p of the global optimal spatial scale is determined, and it is determined that there is an abnormal Moran's I index value when |I|>1.

[0101] Step S013: triggering a review mechanism in the case that the ecosystem service aggregation intensity does not reach a significant level.

[0102] In the embodiment, if the ecosystem service aggregation intensity p of the global optimal spatial scale does not reach a significant level (p≥0.05), an artificial review mechanism is triggered to manually review the global optimal spatial scale.

[0103] Step S014: checking the spatial distribution data of the ecosystem service in the case that there is an abnormal Moran's I index value under the global optimal spatial scale.

[0104] In the embodiment, in the case that there is an abnormal Moran's I index value under the global optimal spatial scale, the spatial distribution data of the ecosystem service is checked to check possible abnormal values or errors, mainly checking the original spatial distribution data of the ecosystem service that causes the abnormality. For example, whether there is an extreme outlier, a null value or a data processing error in the grid data is checked.

[0105] Step S015: determining the difference between the relative closeness degree of each spatial scale and the relative closeness degree of the global optimal spatial scale.

[0106] In the embodiment, in another optional implementation, the difference between the relative closeness degree of each spatial scale and the relative closeness degree of the global optimal spatial scale is determined.

[0107] Step S016: determining each spatial scale whose difference between the relative closeness and the relative closeness of the global optimal spatial scale is lower than the set threshold as the equivalent optimal spatial scale.

[0108] In this embodiment, in the case that the difference between the relative closeness of one spatial scale and the relative closeness of the global optimal spatial scale is lower than the set threshold, the spatial scale is determined as the equivalent optimal spatial scale, which also exists as the best research scale. Finally, the hierarchical achievement output is carried out, and the spatial scale scheme ranking list (containing the relative closeness and the entropy weight weight) and the Moran's I index values of the four types of ecosystems under the optimal spatial scale and the spatial heterogeneity diagnosis abstract are output.

[0109] In this embodiment, the present application can be extended to time series data. By independently running the complete preferred spatial scale process for multiple time nodes (for example, 2005, 2010, 2015, 2020), the time sequence stability of the optimal research scale can be evaluated. If the optimal spatial scale derived in multiple years remains stable, it proves that the spatial scale has universality and robustness for long-term ecosystem service analysis of the research region, thereby enhancing the scientific basis for scale selection.

[0110] It should be noted that, for the method embodiments, the series of actions are described for simplicity, but those skilled in the art should know that the embodiments of the present application are not limited to the order of the actions described, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present application.

[0111] Each of the embodiments in the specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other.

[0112] Those skilled in the art should know that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the embodiments of the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0113] The embodiments of the present application are described with reference to the flowchart illustrations and / or block diagrams of the methods, terminal devices (systems) and computer program products according to the embodiments of the present application. It is understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal devices to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal devices, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0114] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal devices to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal devices, such that a series of operational steps are performed on the computer or other programmable terminal devices to produce a computer implemented process so that the instructions executed on the computer or other programmable terminal devices provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0116] Although preferred embodiments of the present application have been described, those skilled in the art will be able to make additional modifications and variations to these embodiments without departing from the basic inventive concepts. Accordingly, the appended claims are intended to be construed as encompassing all embodiments and modifications that fall within the scope of the present application.

[0117] Finally, it is to be understood that the phraseology or terminology such as "first" and "second" etc. used herein is merely intended to differentiate one entity or operation from another entity or operation, without necessarily requiring or implying any actual such relationship or order between such entities or operations. Moreover, the terms "comprising", "including", or any other closure, are intended to cover the non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements does not include those elements alone but can include other elements not expressly listed or even include elements inherent in such process, method, article, or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0118] The preferred scale selection method for the ecosystem service research provided by the present application is described in detail above, and specific examples are applied herein to illustrate the principles and implementation modes of the present application. The above examples are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed, and the above description should not be understood as a limitation of the present application.

Claims

1. A method for selecting a preferred scale for ecosystem service studies, characterized in that, The method comprises: performing multi-scale spatial analysis unit division on a target region to obtain division results of the target region corresponding to multiple spatial scales; calculating values of various ecosystem service indicators of each spatial analysis unit under each spatial scale according to the division results and obtained geographical data of the target region, wherein the geographical data at least includes land use / land cover data, digital elevation model, climate data and soil data, and the various ecosystem service indicators include water conservation ecosystem service indicators, carbon storage ecosystem service indicators, soil conservation ecosystem service indicators and nitrogen output ecosystem service indicators; constructing a spatial weight matrix under a corresponding spatial scale according to the adjacent relationship of spatial analysis units under the same spatial scale, wherein the spatial weight matrix records the adjacent relationship between each two spatial analysis units; determining Moran's I index values of various ecosystem services under the corresponding spatial scale according to the spatial weight matrix under the same spatial scale and the values of all ecosystem service indicators; constructing a target decision matrix according to the Moran's I index values of various ecosystem services under all spatial scales, wherein the target decision matrix records research performance of various ecosystem services under various spatial scales; determining information entropy of various ecosystem service indicators according to the target decision matrix; determining entropy weight of various ecosystem service indicators according to the information entropy of various ecosystem service indicators; constructing a weighted normalized decision matrix according to the target decision matrix and the entropy weight of various ecosystem service indicators; determining comprehensive distances between each spatial scale and ideal solutions respectively according to the weighted normalized decision matrix, wherein the ideal solutions include positive ideal solutions and negative ideal solutions, the positive ideal solution is the maximum Moran's I value of each ecosystem service in each spatial scale, and the negative ideal solution is the minimum Moran's I value of each ecosystem service in each spatial scale; determining relative closeness between each spatial scale and ideal solutions respectively according to the comprehensive distances of each spatial scale; determining a global optimal spatial scale from all spatial scales according to the relative closeness of each spatial scale.

2. The method of claim 1, wherein the method is used for ecosystem service research. The method comprises: constructing a spatial weight matrix under a corresponding spatial scale according to the adjacent relationship of spatial analysis units under the same spatial scale, comprising: constructing a basic matrix corresponding to all spatial analysis units under the same spatial scale in rows and columns, wherein each row and each column respectively represents a spatial analysis unit; determining whether two spatial analysis units corresponding to each element in the basic matrix are adjacent; in the case that the two spatial analysis units corresponding to the element are adjacent, recording the element as 1, and in the case that the two spatial analysis units corresponding to the element are not adjacent, recording the element as 0, thereby constructing a spatial adjacency matrix; performing standardization processing on each row element of the spatial adjacency matrix to construct a spatial weight matrix, wherein the sum of values of each row data in the spatial weight matrix is 1.

3. A preferred scale selection method for ecosystem service studies according to claim 1, characterized in that, According to the spatial weight matrix under the same spatial scale and the values of all ecosystem service indicators, the Moran's I index value of each type of ecosystem service under the corresponding spatial scale is determined, including: The spatial weight matrix under the same spatial scale and the values of all ecosystem service indicators are calculated through a predefined Moran's I index algorithm, and the Moran's I index value of each type of ecosystem service under the corresponding spatial scale is obtained. The expression of the Moran's I index algorithm is: wherein n is the number of spatial analysis units of the target region at the corresponding spatial scale; is the value of the element corresponding to the i th spatial analysis unit and the j th spatial analysis unit in the spatial weight matrix at the corresponding spatial scale; and are the values of the corresponding category of the ecosystem service index of the i th and j th spatial analysis unit at the corresponding spatial scale, respectively; is the mean value of the values of the corresponding category of the ecosystem service index of all spatial analysis units at the corresponding spatial scale; is the variance of the values of the corresponding category of the ecosystem service index of all spatial analysis units at the corresponding spatial scale.

4. The preferred scale selection method for ecosystem service research according to claim 1, characterized in that, According to the Moran's I index value of each type of ecosystem service under all spatial scales, a target decision matrix is constructed, including: A decision matrix between ecosystem services and spatial scales is constructed, and the rows in the decision matrix represent different spatial scales, and the columns represent the Moran's I index value of each type of ecosystem service. The decision matrix is standardized through a standardization algorithm to obtain a target decision matrix. The expression of the standardization algorithm is: wherein, represents the matrix element in the target decision matrix corresponding to the jth type of ecosystem service under the ith spatial scale; is the Moran's I index value of the jth type of ecosystem service under the ith spatial scale in the decision matrix; m is the total number of spatial scales.

5. The method of claim 1, wherein the method is used for ecosystem service research. According to the target decision matrix, the information entropy of each type of ecosystem service indicator is determined, including: The target decision matrix is calculated through a predefined information entropy determination algorithm to obtain the information entropy of each type of ecosystem service indicator. The expression of the information entropy determination algorithm is: wherein, is the information entropy of the jth type of ecosystem service index; k = 1 / ln(m) is a regulating constant; represents the matrix element corresponding to the jth type of ecosystem service under the ith spatial scale in the target decision matrix; and m is the total number of spatial scales.

6. The method of claim 1, wherein the method is used for ecosystem service research. According to the information entropy of each type of ecosystem service indicator, the entropy weight weight of each type of ecosystem service indicator is determined, including: The information entropy of each type of ecosystem service indicator is calculated through a predefined entropy weight weight determination algorithm to obtain the entropy weight weight of each type of ecosystem service indicator. The expression of the entropy weight weight determination algorithm is: wherein, is the entropy weight of the jth type of ecosystem service index, is the information entropy of the jth type of ecosystem service index, satisfying the constraint condition: .

7. The method of claim 1, wherein the method is used for ecosystem service research. According to the target decision matrix and the entropy weight weight of each type of ecosystem service indicator, a weighted normalized decision matrix is constructed, including: The single elements in the target decision matrix are obtained in sequence. The entropy weight weight of the ecosystem service corresponding to the column of the single element is obtained, and the value of the single element is multiplied by the obtained entropy weight weight to obtain a corresponding target value. According to all target values obtained by calculating all elements in the target decision matrix, a corresponding weighted normalized decision matrix is constructed.

8. The method of claim 1, wherein the method is used for ecosystem service research. According to the weighted normalized decision matrix, the comprehensive distance between each spatial scale and the ideal solution is determined, including: The weighted normalized decision matrix is calculated through a predefined comprehensive distance algorithm to obtain the comprehensive distance between each spatial scale and the ideal solution. The expression of the comprehensive distance algorithm is: wherein, is the positive distance between the ith spatial scale and the positive ideal solution; is the negative distance between the ith spatial scale and the negative ideal solution; is the matrix element in the weighted normalized decision matrix corresponding to the jth ecosystem service under the ith spatial scale, representing the performance value of the ith spatial scale on the jth ecosystem service index; is the positive ideal solution of the jth ecosystem service, representing the maximum Moran's I value of the jth ecosystem service in each spatial scale; is the negative ideal solution of the jth ecosystem service, representing the minimum Moran's I value of the jth ecosystem service in each spatial scale.

9. The method of claim 1, wherein the method is used for ecosystem service research. According to the comprehensive distance of each spatial scale, the relative closeness between each spatial scale and the ideal solution is determined, including: The relative closeness between each spatial scale and the ideal solution is determined through a predefined closeness algorithm. The expression of the closeness algorithm is: wherein, is the relative closeness of the ith spatial scale to the ideal solution, ; is the positive distance between the ith spatial scale and the positive ideal solution, is the negative distance between the ith spatial scale and the negative ideal solution.

10. The method of claim 1, wherein the method is used for ecosystem service research. The method further includes: Determining the ecosystem service aggregation intensity of the global optimal spatial scale, and determining whether there is an abnormal Moran's I index value under the global optimal spatial scale; In the case where the ecosystem service aggregation intensity does not reach a significant level, triggering a review mechanism; In the case of abnormal Moran's I index values at the globally optimal spatial scale, the spatial distribution data of the ecosystem services are checked; determining the difference between the relative closeness of each spatial scale and the relative closeness of the globally optimal spatial scale; determining each spatial scale whose difference between the relative closeness and the relative closeness of the globally optimal spatial scale is lower than a set threshold as an equivalent optimal spatial scale.

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