Composite ecosystem relevance measurement method and system

Abstract

The invention belongs to the technical field of ecological environment protection, and provides a composite ecosystem relevance measurement method and system, and the method comprises the steps: obtaining land utilization remote sensing monitoring data in a research region range, determining the position of a sampling point, obtaining a sampling point land utilization time series data set, constructing a space weight matrix and a time similarity matrix, and carrying out the measurement of the relevance of a composite ecosystem. Calculating each land utilization type to obtain a corresponding local statistic S'i, a significance level value Si, a global statistic S and a significance level value Z, and constructing a relation matrix and a significance test matrix of each land utilization type; the method is based on a theoretical framework of the composite ecosystem, a space-time similarity technology system is introduced, point location space-time characteristics are creatively fused in double-variant correlation analysis, the correlation degree of the two-place type composite ecosystem is calculated, and a new cognition is provided for decoupling an internal complex mechanism of the composite ecosystem.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological environment protection, and in particular relates to a complex ecosystem correlation measurement method and system. Background Art

[0002] Complex ecosystems are integrated systems formed by the interweaving of natural ecosystems and human social systems. With global environmental change and intensified human activities, complex ecosystems face the risk of degradation due to irrational land use and overexploitation of water resources, seriously threatening regional sustainable development. Analysis of land use patterns and their changes can reveal the structural functions and service value of complex ecosystems. Accurately measuring the spatiotemporal correlations of land landscapes is crucial for revealing the spatiotemporal patterns of ecosystem change and the complex evolutionary mechanisms.

[0003] The mountains, rivers, forests, fields, lakes, grasslands, and deserts in natural ecosystems form a living community, with each element interdependent and mutually reinforcing. However, the protection / restoration of woodlands, grasslands, and wetlands often focuses on specialized management, ignoring the interconnectedness and synergistic coupling between them. This lacks holistic and systematic considerations, making it difficult to meet the needs of effective protection, systematic restoration, comprehensive management, and regional sustainable development. Therefore, the technical problem that urgently needs to be solved is to accurately determine the interconnectedness of multi-element complex ecosystems from multiple angles and dimensions. By quantitatively characterizing the spatiotemporal characteristics and inherent correlation mechanisms of the elements representing complex ecosystems, technical support can be provided for the dynamic adaptation and precise regulation of differentiated ecological protection / restoration strategies. Summary of the Invention

[0004] In response to the problems mentioned in the above background technology, the present invention provides a method and system for measuring the correlation of complex ecosystems based on spatiotemporal sequence similarity.

[0005] A complex ecosystem correlation measurement method of the present invention comprises:

[0006] Obtain land use remote sensing monitoring data within the study area;

[0007] Processing the land use remote sensing monitoring data, determining the locations of sample points, and obtaining a land use time series data set of the sample points;

[0008] Constructing a spatial weight matrix and a temporal similarity matrix based on the sample point locations and the sample point land use time series dataset;

[0009] Based on the spatial weight matrix and the temporal similarity matrix, each land use type is calculated to obtain the local statistics S corresponding to each land use type. i ′ , significance coefficient S i, global statistics S and significance coefficient Z value;

[0010] Based on the calculation results, the spatiotemporal similarity coefficients of each land use type were determined, and the correlation of the complex ecosystem within the study area was measured.

[0011] Furthermore, the land use remote sensing monitoring data is processed to determine the sample point locations and obtain a sample point land use time series data set, specifically including:

[0012] According to the land use type, the land use remote sensing monitoring data is reclassified to obtain the land use processing dataset within the study area;

[0013] Extracting sample points from the land use processing dataset based on spatial locations to obtain a sample point land use processing dataset;

[0014] The sample land use processing dataset at each time is processed to obtain the sample land use time series dataset.

[0015] Furthermore, the extracting of sample points from the land use processing dataset based on spatial location to obtain the sample point land use processing dataset specifically includes:

[0016] A geographic grid is constructed based on pixels, regular grid sample points are generated, and processing data of various land use types in the study period and region are extracted; based on spatial location, the sample point locations are determined and stored in the sample point land use processing dataset.

[0017] Furthermore, constructing a spatial weight matrix and a temporal similarity matrix based on the sample point locations and the sample point land use time series dataset specifically includes:

[0018] Based on the sample point locations and the sample point land use time series dataset, a spatial weight matrix is constructed:

[0019]

[0020] Among them, w ij Represents the adjacency weight between i and j in the time series unit STS;

[0021] Construct a distance matrix and dynamically program to align time series:

[0022]

[0023] Where t is the length of the time series, d u,j Represents the element in the i-th row and j-th column of the matrix D, d 1,1 =0, the other values in the first row are The other values in the first column are g is the penalty parameter;

[0024] d i,j =min{d i-1,j-1 +|r i -s i |,d i-1,j +|r i -g|,d i,j-1 +|s i -g|}

[0025] Among them, 2≤i≤t+1, 2≤j≤t+1, d i,j represents the minimum cumulative distance between the first i items of sequence R and the first j items of sequence S; r i ,s i The timestamps in STS-R and STS-S are i, g, and d respectively; t+1,t+1 The value of is the ERP value of sequence R and sequence S. The ERP value is an improved edit distance that measures the difference between time series.

[0026] Convert ERP to normalized similarity score and construct the temporal similarity matrix. The formula is:

[0027]

[0028] Among them, simi(i,j) represents STS i and STS j Similarity between ERP i,j Indicates STS i and STS j ERP values between, max(ERP) and min(ERP) represent the maximum and minimum ERP values, respectively.

[0029] Furthermore, based on the spatial weight matrix and the temporal similarity matrix, each land use type is calculated to obtain the local statistics S corresponding to each land use type. i ′ , significance coefficient S i , global statistics S and significance coefficient Z value, specifically including:

[0030] Based on the spatial weight matrix and the temporal similarity matrix, each land use type is calculated to obtain the local statistic S i ′ , significance level S i :

[0031]

[0032] in, represents the neighborhood mean; s(i) represents the neighborhood standard deviation; n represents the number of neighborhood units; represents the sum of similarities between STS-i and its adjacent STSs; represents the sum of similarities between STS-i and all other STSs;

[0033] Calculate each land use type to obtain the global statistic S and significance level Z value:

[0034]

[0035] in, represents the sum of similarities between STS-i and STS-j, represents the sum of similarities between all STSs, S is a global statistic, E(S) represents the expected similarity in a random situation, and σ(S) represents the standard deviation of the similarity index.

[0036] Furthermore, the temporal and spatial similarity coefficients of each land use type are determined based on the calculation results, and the correlation measurement of the complex ecosystem within the study area is carried out, specifically including:

[0037] According to the local statistics S corresponding to each land use type i ′ , significance coefficient S i , global statistics S and significance coefficient Z value, determine the spatiotemporal similarity coefficient corresponding to each land use type;

[0038] Based on the spatiotemporal similarity coefficients corresponding to the various land use types, a correlation coefficient matrix and a significance test matrix between the various land use types are constructed;

[0039] The formula for calculating the correlation coefficient is:

[0040]

[0041] Among them, x i ,y i represents two sets of observations; represents the mean of the observations;

[0042] The calculation formula for the t statistic is:

[0043]

[0044] Where r represents the correlation coefficient; n represents the sample size;

[0045] Based on the t statistic and the t distribution table, the significance coefficient P is obtained.

[0046] The present invention also provides a complex ecosystem correlation measurement system, the system comprising:

[0047] Acquisition module, used to obtain land use remote sensing monitoring data within the study area;

[0048] A processing module is used to process the land use remote sensing monitoring data, determine the sample point location, and obtain a sample point land use time series data set;

[0049] A construction module, configured to construct a spatial weight matrix and a temporal similarity matrix based on the sample point locations and the sample point land use time series dataset;

[0050] The first calculation module is used to calculate each land use type based on the spatiotemporal weight matrix and the temporal similarity matrix to obtain the local statistics S corresponding to each land use type. i ′ , significance coefficient S i , global statistics S and significance coefficient Z value;

[0051] The second calculation module is used to determine the spatiotemporal similarity coefficients of each land use type based on the calculation results and to measure the correlation of the complex ecosystem within the study area.

[0052] Furthermore, the building blocks are specifically used to:

[0053] Based on the sample point locations and the sample point land use time series dataset, a spatial weight matrix is constructed:

[0054]

[0055] Among them, w ij Represents the adjacency weight between i and j in the time series unit STS;

[0056] Construct a distance matrix and dynamically program to align time series:

[0057]

[0058] Where t is the length of the time series, d i,j Represents the element in the i-th row and j-th column of the matrix D, d 1,1 =0, the other values in the first row are The other values in the first column are g is the penalty parameter;

[0059] d i,j =min{d i-1,j-1 +|r i -s i |,d i-1,j +|ri -g|,d i,j-1 +|s i -g|}

[0060] Among them, 2≤i≤t+1, 2≤j≤t+1, d i,j represents the minimum cumulative distance between the first i items of sequence R and the first j items of sequence S; r i ,s i The timestamps in STS-R and STS-S are i, g, and d respectively; t+1,t+1 The value of is the ERP value of sequence R and sequence S. The ERP value is an improved edit distance that measures the difference between time series.

[0061] Convert ERP to normalized similarity score and construct the temporal similarity matrix. The formula is:

[0062]

[0063] Among them, simi(i,j) represents STS i and STS j Similarity between ERP i,j Indicates STS i and STS j ERP values between, max(ERP) and min(ERP) represent the maximum and minimum ERP values, respectively.

[0064] Furthermore, the first calculation module is specifically configured to:

[0065] Based on the spatial weight matrix and the temporal similarity matrix, each land use type is calculated to obtain the local statistic S i ′ , significance level S i :

[0066]

[0067] in, represents the neighborhood mean; s(i) represents the neighborhood standard deviation; n represents the number of neighborhood units; represents the sum of similarities between STS-i and its adjacent STSs; represents the sum of similarities between STS-i and all other STSs;

[0068] Calculate each land use type to obtain the global statistic S and significance level Z value:

[0069]

[0070] in, represents the sum of similarities between STS-i and STS-j, represents the sum of similarities between all STSs, S is a global statistic, E(S) represents the expected similarity in a random situation, and σ(S) represents the standard deviation of the similarity index.

[0071] Furthermore, the second calculation module is specifically configured to:

[0072] According to the local statistics S corresponding to each land use type i ′ , significance coefficient S i , global statistics S and significance coefficient Z value, determine the spatiotemporal similarity coefficient corresponding to each land use type;

[0073] Based on the spatiotemporal similarity coefficients corresponding to the various land use types, a correlation coefficient matrix and a significance test matrix between the various land use types are constructed;

[0074] The formula for calculating the correlation coefficient is:

[0075]

[0076] Among them, x i ,y i represents two sets of observations; represents the mean of the observations;

[0077] The calculation formula for the t statistic is:

[0078]

[0079] Where r represents the correlation coefficient; n represents the sample size;

[0080] Based on the t statistic and the t distribution table, the significance coefficient P is obtained.

[0081] Compared with the prior art, the present invention has the following advantages:

[0082] The present invention provides a method and system for measuring the correlation of complex ecosystems based on the similarity of spatiotemporal sequences. Based on the theoretical framework of complex ecosystems, it incorporates the spatiotemporal similarity technology system, creatively integrates the spatiotemporal characteristics of points into the bivariate correlation analysis, and then calculates the correlation between the two types of complex ecosystems. It provides new cognition for decoupling the complex mechanisms within the complex ecosystem, provides new technical processes for accurately determining the correlation of complex ecosystems from multiple angles and dimensions, and provides strong technical support for providing precise dynamic restoration plans for differentiated ecological protection and restoration strategies. It also has the significant advantages of being easy to operate and easy to understand. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0084] Figure 1 Schematic diagram of a process for measuring the correlation of a complex ecosystem according to the present invention;

[0085] Figure 2 Schematic diagram of a theoretical model of a complex ecosystem according to an embodiment of the present invention;

[0086] Figure 3 This is a flow chart of complex ecosystem correlation measurement according to an embodiment of the present invention;

[0087] Figure 4 This is a technical flow chart of the spatiotemporal sequence similarity ERP algorithm according to an embodiment of the present invention;

[0088] Figure 5 Schematic diagram of the structure of a complex ecosystem correlation measurement system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0089] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0090] The terms "first," "second," "third," "fourth," and the like (if any) in the description and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions, for example, a process comprising a series of steps or methods is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes or methods.

[0091] The following is an explanation of the technical principles of the present invention:

[0092] The present invention integrates the principles of geographic information science, statistics, ecology, etc. Its core theoretical basis includes the ERP algorithm for single land type spatiotemporal data extracted based on land use classification to build a spatiotemporal sequence similarity matrix, coupled to form a spatial weight matrix, and constructing global (S value) and local (S i The spatial association statistics (values) were used to quantify the spatiotemporal dependence structure of grassland / sand dynamics; the significance of complex ecosystems was judged and the correlation of complex ecosystems was quantified based on the spatiotemporal similarity association statistics and the significance of the correlation between the six land types.

[0093] The core functional modules of the present invention include the similarity of single land class spatiotemporal sequences and the quantification of inter-land class correlations, specifically:

[0094] The core principle of spatio-temporal sequence (STS) similarity of single land types is to further calculate the global statistic S, local statistic Si, and significance level of single land types in the region based on the similarity matrix constructed from the time series of sampling point data and the spatial weight matrix formed by the spatial coordinates of the sampling points. Among them, a high S value indicates that adjacent STSs are highly similar (positive spatial correlation), while a low S value indicates the opposite. Si measures the degree of similarity clustering between a single STS and its neighborhood, which is used to identify local high-similarity or low-similarity clusters.

[0095] The similarity of spatiotemporal sequences (STS) focuses on the similarity analysis of time series data at a fixed spatial position, which refers to the similarity between different time series data at a fixed spatial position. The present invention measures this by calculating the edit distance with real penalty (ERP). ERP calculates the similarity between STSs by aligning the time axis through dynamic programming and introducing a penalty parameter (g). The ERP value is the output result, which is converted into a similarity score through normalization to construct a similarity matrix simi(i,j), where i and j represent different spatiotemporal sequences, respectively, reflecting the temporal dynamic similarity of the spatiotemporal sequence pair.

[0096] The binary weight matrix is a spatial correlation matrix of sampling points formed based on their spatial coordinates. It defines the spatial adjacency between spatiotemporal sequences. It uses spatial constraints to confine temporal similarity to a spatial neighborhood, thereby quantifying spatial aggregation or dispersion patterns.

[0097] The core principle of quantifying inter-class correlations lies in calculating the correlation coefficients and significance tests between the spatiotemporal similarities of the ERPs of land use types. Based on the statistical concepts of covariance and standardization, this method standardizes the covariance to eliminate dimensionality effects and compresses the linear correlation strength between variables to the interval [-1, 1]. This aims to quantify the degree of linear correlation between two continuous variables. This analytical method reveals the inherent correlation mechanism between variables by calculating the correlation coefficient between them.

[0098] In one embodiment of the present invention, a method for measuring the correlation of a complex ecosystem is provided, such as Figure 1 As shown, the method includes the following steps:

[0099] S1. Obtain remote sensing monitoring data of land use within the study area.

[0100] In this embodiment, multi-temporal land use remote sensing monitoring data of the whole country is obtained, the data is imported into a geographic information system platform, and clipped to obtain land use remote sensing monitoring data within the study area.

[0101] S2. Processing the land use remote sensing monitoring data, determining the locations of sample points, and obtaining a land use time series dataset of the sample points.

[0102] In this embodiment, step S2 processes the land use remote sensing monitoring data, determines the sample point locations, and obtains a sample point land use time series dataset, including the following steps:

[0103] S21. Reclassify the land use remote sensing monitoring data according to the land use type, construct a land type map, and obtain a land use processing data set within the study area;

[0104] In this embodiment, the land use types include cultivated land, grassland, woodland, sandy land, wetland, and construction land. Figure 2 This is a schematic diagram of the theoretical model of a complex ecosystem according to an embodiment of the present invention. The basic structural unit of a complex ecosystem is a second-order Rubik's Cube, and the corner blocks of the Rubik's Cube are ecological characterization modules, which represent six types of land use: cultivated land, grassland, woodland, sandy land, wetland, and construction land. The twisting, flipping, rotation, and restoration of the Rubik's Cube are the nonlinear interaction forces that drive the operation of the complex ecosystem. Complex ecosystems present various surface landscape characteristics, i.e., land use status, through the interactive operation of ecosystem structure and service space and time. The present invention further explores the nonlinear operation mechanism within complex systems by quantifying ecological characterization modules, and further provides technical support for the protection and restoration of fragile ecosystems.

[0105] In this embodiment, in a geographic information system (GIS), reclassification refers to reallocating values in a dataset into different categories or levels according to specific rules or standards.

[0106] In this embodiment, the land type map refers to an image in a geographic information system (GIS) that represents different land use types on a map using different colors or symbols. It is an intuitive visualization tool used to display the spatial distribution characteristics of land use.

[0107] S22. Extracting sample points from the land use processing dataset based on spatial locations to obtain a sample point land use processing dataset;

[0108] In this embodiment, a geographic grid is constructed based on pixels, regular grid samples are generated, and processing data of various land use types in the study period and region are extracted; wherein the pixel width and height of the geographic grid can be set to X.

[0109] In this embodiment, based on the spatial location, the sample point location is determined and stored in the sample point land use processing dataset, and the sample point land use processing dataset is stored in a text file. Determining the sample point location means writing the vertex coordinates of the sample point into the sample point land use processing dataset based on the spatial coordinates to derive the spatial coordinates of the sample point.

[0110] S23. Process the sample land use processing dataset at each time to obtain a sample land use time series dataset.

[0111] In this embodiment, the sample point land use processing dataset is the sample point land use data for a single time point, including the sample point ID, sample point location, and land use type information at that time point; the sample point land use time series dataset is the sample point land use data for multiple time points, including the sample point ID, sample point location, and land use type information at each time point, and is obtained by integrating multiple sample point land use processing datasets.

[0112] S3. Constructing a spatial weight matrix and a temporal similarity matrix based on the sample point locations and the sample point land use time series dataset.

[0113] In this embodiment, step S3 constructs a spatial weight matrix and a temporal similarity matrix based on the sample point locations and the sample point land use time series dataset, including the following steps:

[0114] Based on the sample point locations and the sample point land use time series dataset, a spatial weight matrix is constructed:

[0115]

[0116] Among them, w ij Represents the adjacency weight of i and j in the time series unit STS; STS-i represents the i-th row in the time series unit STS, and STS-j represents the j-th row in the time series unit STS;

[0117] Construct a distance matrix and dynamically program to align time series:

[0118]

[0119] Where t is the length of the time series, d i,j Represents the element in the i-th row and j-th column of the matrix D, d 1,1 =0, the other values in the first row are The other values in the first column are g is the penalty parameter;

[0120] d i,j =min{d i-1,j-1 +|r i -s i |,d i-1,j +|r i -g|,d i,j-1 +|s i -g|}

[0121] Among them, 2≤i≤t+1, 2≤j≤t+1, d i,j represents the minimum cumulative distance between the first i items of sequence R and the first j items of sequence S; r i ,s i The timestamps in STS-R and STS-S are i, g, and g, respectively; they represent the gap penalty value, which is used to handle the inconsistency of sequence lengths; d t+1,t+1 The value of is the ERP value of sequence R and sequence S. The ERP value is an improved edit distance that measures the difference between time series.

[0122] Convert ERP to normalized similarity score and construct the temporal similarity matrix. The formula is:

[0123]

[0124] Among them, simi(i,j) represents STS i and STS j Similarity between ERP i,j Indicates STS i and STS j ERP values between, max(ERP) and min(ERP) represent the maximum and minimum ERP values, respectively.

[0125] S4. Based on the spatial weight matrix and the temporal similarity matrix, each land use type is calculated to obtain the local statistics S corresponding to each land use type. i ′ , significance coefficient S i , global statistic S and significance coefficient Z value.

[0126] In this embodiment, step S4 calculates each land use type based on the spatial weight matrix and the temporal similarity matrix, including the following steps:

[0127] Based on the spatial weight matrix and the temporal similarity matrix, each land use type is calculated to obtain the local statistic S i ′ , significance level S i :

[0128]

[0129] in, represents the neighborhood mean; s(i) represents the neighborhood standard deviation; n represents the number of neighborhood units; represents the sum of similarities between STS-i and its adjacent STSs; represents the sum of similarities between STS-i and all other STSs;

[0130] Calculate each land use type to obtain the global statistic S and significance level Z value:

[0131]

[0132] in, represents the sum of similarities between STS-i and STS-j, represents the sum of similarities between all STSs, S is a global statistic, E(S) represents the expected similarity in a random situation, and σ(S) represents the standard deviation of the similarity index.

[0133] S5. Determine the spatiotemporal similarity coefficients of each land use type based on the calculation results, and measure the correlation of the complex ecosystem within the study area.

[0134] In this embodiment, step S5 determines the spatiotemporal similarity coefficient of each land use type based on the calculation results, and performs a correlation measurement on the complex ecosystem within the study area, including the following steps:

[0135] According to the local statistics S corresponding to each land use type i ′ , significance coefficient S i, global statistics S and significance coefficient Z value, determine the spatiotemporal similarity coefficient corresponding to each land use type;

[0136] Based on the spatiotemporal similarity coefficients corresponding to the various land use types, a correlation coefficient matrix and a significance test matrix between the various land use types are constructed to determine the linear correlation between the two land types and to quantify the spatiotemporal correlation between the two land types;

[0137] The formula for calculating the correlation coefficient is:

[0138]

[0139] Among them, x i ,y i represents two sets of observations; represents the mean of the observations;

[0140] The calculation formula for the t statistic is:

[0141]

[0142] Where r represents the correlation coefficient; n represents the sample size;

[0143] Based on the t statistic and the t distribution table, the significance coefficient P is obtained, that is, the significance test of the correlation coefficient.

[0144] In this embodiment, the correlation coefficient matrix between the various land use types refers to a matrix constructed by the correlation coefficients between the various land use types.

[0145] In this embodiment, Figure 4 This is a technical flow chart of the spatiotemporal sequence similarity ERP algorithm of an embodiment of the present invention. First, a long-term multi-period land use type spatial distribution dataset is obtained, and time series similarity analysis is performed to identify temporal pattern changes. A spatial adjacency weight matrix is constructed to quantify spatial correlation, and finally spatial weighted similarity is obtained. Global spatial correlation analysis, local spatial correlation analysis, and spatial autocorrelation are then performed. Spatial autocorrelation test is performed using the S test, and finally the promotion or inhibition effect is judged. The classification results and intensity indicators of positive, negative, and uncorrelated patterns are output, and corresponding human activity adjustment strategies are proposed to reveal the complex relationship network in spatiotemporal data and provide scientific basis and technical support for decision makers.

[0146] Example 1

[0147] Figure 3 This is a flow chart of the complex ecosystem correlation measurement according to an embodiment of the present invention, as shown in FIG. Figure 3 As shown, the method described in this embodiment includes:

[0148] Obtain 10 periods of national multi-temporal land use remote sensing monitoring data for a certain location and its surrounding areas from 1980 to 2020. Use Global Mapper software to load the raw data and perform the following operations:

[0149] Clipping boundary: Clip the original data to obtain the study area range data.

[0150] Land use type reclassification: For the six land use types within the study period and region, we reconstructed the atlas of each land use type according to the characteristics of grassland, sandy land, wetland, forest land, building land, and cultivated land to form a land use atlas dataset for the study area. Among them, the land use types are referred to as the six land use types.

[0151] Geographic grid construction: A geographic grid is constructed based on pixels to generate regular grid sample points. The pixel width and height are set to 1000m, for a total of 836 sample points. The sample data in Table 1-5 only shows the first 20.

[0152] Grid map information extraction: Extract the map information of the six land types in the study period and region to sample points to form a sample point land use map dataset.

[0153] Determine spatial location: Based on the spatial location, write the geographic coordinates of the sample point into the sample point land use map dataset.

[0154] Output data: Output the land use map dataset of the sample point and save it as a tif file.

[0155] The above operations are repeated to process the sample land use atlas dataset at each time point to obtain the sample land use time series dataset.

[0156] Single land type spatiotemporal sequence similarity analysis: Calculate and analyze the sample land use map dataset. The specific operations are as follows:

[0157] Using the formula Build the spatial weight matrix, see Table 1;

[0158] Table 1 Spatial weight matrix

[0159]

[0160] Using the formula d i,j =min{d i-1,j-1 +|r i -s i |,d i-1,j +|r i -g|,d i,j-1 +|s i -g|}, Construct a spatiotemporal similarity matrix.

[0161] Based on the spatial weight matrix and similarity matrix, using the formula Calculate the global autocorrelation coefficient S, significance coefficient Z value, local spatiotemporal autocorrelation coefficient S value, significance level value s of the six land types respectively i , see Table 2-Table 4.

[0162] Table 2 Global autocorrelation coefficient S value and significance coefficient z value

[0163]

[0164] Table 3 Local spatiotemporal autocorrelation coefficient S values

[0165]

[0166] Table 4 Significance level of local spatiotemporal autocorrelation coefficients i

[0167]

[0168] Quantitative analysis of correlation between land types: Based on the spatiotemporal autocorrelation ERP values of the six land types, the formula as well as The correlation coefficient r and significance coefficient P among the six land types were calculated to obtain the correlation analysis results among the land types, see Table 5.

[0169] Table 5. Significance level of spatiotemporal autocorrelation S-value

[0170]

[0171] Table 5 shows the correlation analysis results among the six land use types, including the correlation coefficient r and significance level P value. The correlation coefficient r ranges from -1 to 1, indicating positive correlation, negative correlation, or no correlation. The significance level P value indicates the significance of the correlation; smaller the P value, the more significant the correlation.

[0172] According to the above operation and principle, the measurement results were analyzed and it was found that grassland-sand land use type is a highly correlated land type in arid and semi-arid areas. The specific analysis is as follows:

[0173] According to the S-value significance coefficient analysis of the six land types, the significance coefficient P value between grassland and sandy land is <0.001, which is very significant. It can be seen that grassland and sandy land are highly correlated in this study area, while the significance coefficient values between other land types are low. Grassland and cultivated land, grassland and wetland, wetland and sandy land are only weakly correlated, and other land use types are not correlated.

[0174] This result is consistent with the antagonistic relationship pattern between grassland and sandy land in the arid and semi-arid environment of the study area, further verifying the correctness of the method.

[0175] Based on the above method, an embodiment of the present invention provides a complex ecosystem correlation measurement system. This system couples geographic information computing module resources to implement complex ecosystem correlation measurement functions, providing reliable results. The embodiment of this device corresponds to the aforementioned method embodiment. For ease of reading, this embodiment will not further elaborate on the details of the aforementioned method embodiment. However, it should be understood that the system in this embodiment is capable of implementing all of the aforementioned method embodiments.

[0176] like Figure 5 As shown, the system includes:

[0177] Acquisition module 501, used to acquire land use remote sensing monitoring data within the study area;

[0178] The processing module 502 is used to process the land use remote sensing monitoring data, determine the sample point location, and obtain the sample point land use time series data set;

[0179] A construction module 503 is used to construct a spatial weight matrix and a temporal similarity matrix based on the sample point locations and the sample point land use time series dataset;

[0180] The first calculation module 504 is used to calculate each land use type based on the spatial weight matrix and the temporal similarity matrix to obtain the local statistics S corresponding to each land use type. i ′ , significance coefficient S i , global statistics S and significance coefficient Z value;

[0181] The second calculation module 505 is used to determine the spatiotemporal similarity coefficient of each land use type according to the calculation results, and to measure the correlation of the complex ecosystem within the study area.

[0182] The processing module is specifically used for:

[0183] A classification unit is used to reclassify the land use remote sensing monitoring data according to land use types to obtain a land use processing dataset within the study area;

[0184] An extraction unit, configured to extract sample points from the land use processing dataset based on spatial locations to obtain a sample point land use processing dataset;

[0185] The data processing unit is used to process the sample land use processing data set at each time to obtain the sample land use time series data set.

[0186] The building blocks are specifically used for:

[0187] Based on the sample point locations and the sample point land use time series dataset, a spatial weight matrix is constructed:

[0188]

[0189] Among them, w ij Represents the adjacency weight between i and j in the time series unit STS;

[0190] Construct a distance matrix and dynamically program to align time series:

[0191]

[0192] Where t is the length of the time series, d i,j Represents the element in the i-th row and j-th column of the matrix D, d 1,1 =0, the other values in the first row are The other values in the first column are g is the penalty parameter;

[0193] d i,j =min{d i-1,j-1 +|r i -s i |,d i-1,j +|r i -g|,d i,j-1 +|s i -g|}

[0194] Among them, 2≤i≤t+1, 2≤j≤t+1, d i,j represents the minimum cumulative distance between the first i items of sequence R and the first j items of sequence S; r i ,s i The timestamps in STS-R and STS-S are i, g, and g, respectively; they represent the gap penalty value, which is used to handle the inconsistency of sequence lengths; d t+1,t+1 The value of is the ERP value of sequence R and sequence S. The ERP value is an improved edit distance that measures the difference between time series.

[0195] Convert ERP to normalized similarity score and construct the temporal similarity matrix. The formula is:

[0196]

[0197] Among them, simi(i,j) represents STS i and STS j Similarity between ERP i,j Indicates STS i and STS j ERP values between, max(ERP) and min(ERP) represent the maximum and minimum ERP values, respectively.

[0198] The first calculation module is specifically configured to:

[0199] Based on the spatial weight matrix and the temporal similarity matrix, each land use type is calculated to obtain the local statistic S i ′ , significance level S i :

[0200]

[0201] in, represents the neighborhood mean; s(i) represents the neighborhood standard deviation; n represents the number of neighborhood units; represents the sum of similarities between STS-i and its adjacent STSs; represents the sum of similarities between STS-i and all other STSs;

[0202] Calculate each land use type to obtain the global statistic S and significance level Z value:

[0203]

[0204] in, represents the sum of similarities between STS-i and STS-j, represents the sum of similarities between all STSs, S is a global statistic, E(S) represents the expected similarity in a random situation, and σ(S) represents the standard deviation of the similarity index.

[0205] The second calculation module is specifically configured to:

[0206] According to the local statistics S corresponding to each land use type i ′ , significance coefficient S i, global statistics S and significance coefficient Z value, determine the spatiotemporal similarity coefficient corresponding to each land use type;

[0207] Based on the spatiotemporal similarity coefficients corresponding to the various land use types, a correlation coefficient matrix and a significance test matrix between the various land use types are constructed;

[0208] The formula for calculating the correlation coefficient is:

[0209]

[0210] Among them, x i ,y i represents two sets of observations; represents the mean of the observations;

[0211] According to the significance test matrix, the linear correlation between the two land classes is determined, and the temporal and spatial correlation between the two land classes is quantified. The calculation formula of the t statistic is:

[0212]

[0213] Where r represents the correlation coefficient; n represents the sample size;

[0214] Based on the t statistic and the t distribution table, the significance coefficient P is obtained, that is, the significance test of the correlation coefficient.

[0215] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for measuring the correlation of complex ecosystems, characterized in that: The method comprises: Obtain land use remote sensing monitoring data within the study area; Processing the land use remote sensing monitoring data, determining the locations of sample points, and obtaining a land use time series data set of the sample points; Constructing a spatial weight matrix and a temporal similarity matrix based on the sample point locations and the sample point land use time series dataset; Based on the spatial weight matrix and the temporal similarity matrix, each land use type is calculated to obtain the local statistics S corresponding to each land use type. i ′ , significance coefficient S i , global statistics S and significance coefficient Z value; Based on the calculation results, the spatiotemporal similarity coefficients of each land use type were determined, and the correlation of the complex ecosystem within the study area was measured.

2. The method according to claim 1, characterized in that The processing of the land use remote sensing monitoring data to determine the sample point locations and obtain the sample point land use time series data set specifically includes: According to the land use type, the land use remote sensing monitoring data is reclassified to obtain the land use processing dataset within the study area; Extracting sample points from the land use processing dataset based on spatial locations to obtain a sample point land use processing dataset; The sample land use processing dataset at each time is processed to obtain the sample land use time series dataset.

3. The method according to claim 2, characterized in that The extracting of sample points from the land use processing dataset based on spatial location to obtain the sample point land use processing dataset specifically includes: A geographic grid is constructed based on pixels, regular grid sample points are generated, and processing data of various land use types in the study period and region are extracted; based on spatial location, the sample point locations are determined and stored in the sample point land use processing dataset.

4. The method according to claim 1, wherein The step of constructing a spatial weight matrix and a temporal similarity matrix based on the sample point locations and the sample point land use time series dataset specifically includes: Based on the sample point locations and the sample point land use time series dataset, a spatial weight matrix is constructed: Among them, w ij Represents the adjacency weight between i and j in the time series unit STS; Construct a distance matrix and dynamically program to align time series: Where t is the length of the time series, d i,j Represents the element in the i-th row and j-th column of the matrix D, d 1,1 =0, the other values in the first row are The other values in the first column are g is the penalty parameter; d i,j =min{d i-1,j-1 +|r i -s i |,d i-1,j +|r i -g|,d i,j-1 +|s i -g|} Among them, 2≤i≤t+1, 2≤j≤t+1, d i,j represents the minimum cumulative distance between the first i items of sequence R and the first j items of sequence S; r i ,s i The timestamps in STS-R and STS-S are i, g, and d respectively; t+1,t+1 The value of is the ERP value of sequence R and sequence S. The ERP value is an improved edit distance that measures the difference between time series. Convert ERP to normalized similarity score and construct the temporal similarity matrix. The formula is: Among them, simi(i,j) represents STS i and STS j Similarity between ERP i,j Indicates STS i and STS j ERP values between, max(ERP) and min(ERP) represent the maximum and minimum ERP values, respectively.

5. The method according to claim 1, wherein Based on the spatial weight matrix and the temporal similarity matrix, each land use type is calculated to obtain the local statistics S corresponding to each land use type. i ′ , significance coefficient S i , global statistics S and significance coefficient Z value, specifically including: Based on the spatial weight matrix and the temporal similarity matrix, each land use type is calculated to obtain the local statistic S i ′ , significance level S i : in, represents the neighborhood mean; s(i) represents the neighborhood standard deviation; n represents the number of neighborhood units; represents the sum of similarities between STS-i and its adjacent STSs; Represents the sum of the similarities between STS-i and all other STSs; it is calculated for each land use type to obtain the global statistic S and significance level Z value: in, represents the sum of similarities between STS-i and STS-j, represents the sum of similarities between all STSs, S is a global statistic, E(S) represents the expected similarity in a random situation, and σ(S) represents the standard deviation of the similarity index.

6. The method according to claim 1, characterized in that The temporal and spatial similarity coefficients of each land use type are determined based on the calculation results, and the correlation measurement of the complex ecosystem within the study area is carried out, specifically including: According to the local statistics S corresponding to each land use type i ′ , significance coefficient S i , global statistics S and significance coefficient Z value, determine the spatiotemporal similarity coefficient corresponding to each land use type; Based on the spatiotemporal similarity coefficients corresponding to the various land use types, a correlation coefficient matrix and a significance test matrix between the various land use types are constructed; The formula for calculating the correlation coefficient is: Among them, x i ,y i represents two sets of observations; represents the mean of the observations; The calculation formula for the t statistic is: Where r represents the correlation coefficient; n represents the sample size; Based on the t statistic and the t distribution table, the significance coefficient P is obtained.

7. A complex ecosystem correlation measurement system, characterized by: The system comprises: Acquisition module, used to obtain land use remote sensing monitoring data within the study area; A processing module is used to process the land use remote sensing monitoring data, determine the sample point location, and obtain a sample point land use time series data set; A construction module, configured to construct a spatial weight matrix and a temporal similarity matrix based on the sample point locations and the sample point land use time series dataset; The first calculation module is used to calculate each land use type based on the spatiotemporal weight matrix and the temporal similarity matrix to obtain the local statistics S corresponding to each land use type. i ′ , significance coefficient S i , global statistics S and significance coefficient Z value; The second calculation module is used to determine the spatiotemporal similarity coefficients of each land use type based on the calculation results and to measure the correlation of the complex ecosystem within the study area.

8. The system according to claim 7, characterized in that The building blocks are specifically used for: Based on the sample point locations and the sample point land use time series dataset, a spatial weight matrix is constructed: Among them, w ij Represents the adjacency weight between i and j in the time series unit STS; Construct a distance matrix and dynamically program to align time series: Where t is the length of the time series, d i,j Represents the element in the i-th row and j-th column of the matrix D, d 1,1 =0, the other values in the first row are The other values in the first column are g is the penalty parameter; d i,j =min{d i-1,j-1 +|r i -s i |,d i-1,j +|r i -g|,d i,j-1 +|s i -g|} Among them, 2≤i≤t+1, 2≤j≤t+1, d i,j represents the minimum cumulative distance between the first i items of sequence R and the first j items of sequence S; r i ,s i The timestamps in STS-R and STS-S are i, g, and d respectively; t+1,t+1 The value of is the ERP value of sequence R and sequence S. The ERP value is an improved edit distance that measures the difference between time series. Convert ERP to normalized similarity score and construct the temporal similarity matrix. The formula is: Among them, simi(i,j) represents STS i and STS j Similarity between ERP i,j Indicates STS i and STS j ERP values between, max(ERP) and min(ERP) represent the maximum and minimum ERP values, respectively.

9. The system according to claim 7, wherein: The first calculation module is specifically configured to: Based on the spatial weight matrix and the temporal similarity matrix, each land use type is calculated to obtain the local statistic S i ′ , significance level S i : in, represents the neighborhood mean; s(i) represents the neighborhood standard deviation; n represents the number of neighborhood units; represents the sum of similarities between STS-i and its adjacent STSs; Represents the sum of the similarities between STS-i and all other STSs; it is calculated for each land use type to obtain the global statistic S and significance level Z value: in, represents the sum of similarities between STS-i and STS-j, represents the sum of similarities between all STSs, S is a global statistic, E(S) represents the expected similarity in a random situation, and σ(S) represents the standard deviation of the similarity index.

10. The system according to claim 7, wherein: The second calculation module is specifically configured to: According to the local statistics S corresponding to each land use type i ′ , significance coefficient S i , global statistics S and significance coefficient Z value, determine the spatiotemporal similarity coefficient corresponding to each land use type; Based on the spatiotemporal similarity coefficients corresponding to the various land use types, a correlation coefficient matrix and a significance test matrix between the various land use types are constructed; The formula for calculating the correlation coefficient is: Among them, x i ,y i represents two sets of observations; represents the mean of the observations; The calculation formula for the t statistic is: Where r represents the correlation coefficient; n represents the sample size; Based on the t statistic and the t distribution table, the significance coefficient P is obtained.

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