Method for evaluating farmland ecosystem services based on remote sensing monitoring

Abstract

The present application relates to the technical field of ecosystem service evaluation, in particular to a cultivated land ecosystem service evaluation method based on remote sensing monitoring, comprising the following steps: obtaining multi-dimensional cultivated land data of the evaluation area by using remote sensing technology, and performing data dimension reduction processing on the collected data, while introducing sparsity constraint to reduce the interference of redundant features in the data set; realizing cultivated land ecological service evaluation according to the collected data; intelligently optimizing based on the cooperative relationship between data; the present application collects multi-dimensional cultivated land data and performs dimension reduction processing combined with sparsity constraint, providing high-quality data basis for subsequent ecological service evaluation; based on the cooperative relationship analysis between data, the cooperative effect coefficient and the weight dynamic allocation technology are used to improve the adaptability of cultivated land ecosystem service evaluation; combined with the function function and the weight optimization method, the quantitative comprehensive evaluation of soil conservation, water conservation, carbon fixation and other services is realized, and the scientificity and universality of the evaluation model are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecosystem service evaluation, in particular to a cultivated land ecosystem service evaluation method based on remote sensing monitoring. BACKGROUND

[0002] Cultivated land ecosystem service evaluation refers to quantifying, analyzing and evaluating the ability of cultivated land to provide ecosystem services through scientific methods. However, at present, the following problems are faced when evaluating cultivated land ecosystem services through remote sensing technology:

[0003] Remote sensing data comes from various sources, and the spatial resolution, temporal resolution, band setting and observation angle of each data are different, making it difficult to directly and uniformly use the data for ecosystem service evaluation;

[0004] The evaluation of ecosystem services (such as soil conservation, water conservation, carbon sequestration, habitat maintenance, wind and sand prevention) requires high-quality input data, and there are differences between the parameters (such as NDVI, land cover type, etc.) in remote sensing images and the actual situation, affecting the accuracy and regional applicability of the calculation results. SUMMARY

[0005] In view of the above problems, the present application is proposed.

[0006] To solve the above technical problems, the present application provides the following technical scheme: a cultivated land ecosystem service evaluation method based on remote sensing monitoring, comprising the following steps,

[0007] Using remote sensing technology to obtain multi-dimensional cultivated land data of the evaluation area, and performing data dimension reduction processing on the collected data, while introducing sparsity constraints to reduce the interference of redundant features in the data set;

[0008] According to the collected data, the cultivated land ecological service evaluation is realized, specifically:

[0009] Based on the spatial analysis and quantitative estimation of the cultivated land ecological service system, a cultivated land ecological service evaluation model is built, and the cultivated land data is used as the input data of the model. According to the output results of the model, the cultivated land ecological service evaluation is realized, which also includes,

[0010] When comprehensively evaluating the cultivated land ecological services of different regions, the distribution rules of the ecological service weight coefficients are formulated to realize the dynamic distribution of the weight coefficients;

[0011] Intelligent optimization is carried out based on the cooperative relationship between data, specifically:

[0012] According to the data correlation analysis between ecological services, the synergistic effect coefficient between two service functions is determined, the cultivated land ecological service evaluation model is intelligently optimized according to the determined synergistic effect coefficient, and whether the optimization based on service deviation is completed is judged.

[0013] As a preferred scheme of the cultivated land ecological system service evaluation method based on remote sensing monitoring, the cultivated land ecological service evaluation model is specifically built as follows:

[0014] The to-be-evaluated region is divided into a two-dimensional grid network, the grid unit is g, and each grid unit has different ecological attributes, including vegetation index, soil moisture and slope data, so that,

[0015] G={g i,j |i=1,2,3,...,M;j=1,2,3,...,N}

[0016] Wherein, G represents the two-dimensional grid network divided by the to-be-evaluated region, M and N represent the rows and columns corresponding to the grid network respectively, i and j represent the sequence number indexes of the rows and columns of the grid units in the grid network;

[0017] According to different cultivated land ecological services, a function function f k (g i,j ) is set, and the service value of each service in the grid unit is calculated according to the set function function, so that,

[0018]

[0019] Wherein, f k (g i,j ) represents the function function corresponding to the kth ecological service, M and N represent the rows and columns corresponding to the grid network respectively, i and j represent the grid units in the grid network, and S k represents the service value of the kth ecological service in the grid unit.

[0020] According to the requirements of the to-be-evaluated region, different ecological services are allocated corresponding weight coefficients w k , and the comprehensive service value is calculated, so that,

[0021]

[0022] Wherein, S k represents the service value of the kth ecological service in the grid unit, K represents the upper limit of the number of ecological services in the to-be-evaluated region, w k represents the weight coefficient allocated to different ecological services, and S total represents the calculated comprehensive service value.

[0023] As a preferred scheme of the cultivated land ecosystem service evaluation method based on remote sensing monitoring, the distribution rule for determining the ecological service weight coefficient is specifically as follows:

[0024] An initial priority weight p is allocated to each ecological service according to the needs of the region to be evaluated k , the value range is [0, 1], and the formula is satisfied:

[0025]

[0026] , wherein p k represents the initial priority weight of the kth ecological service, and K represents the upper limit of the number of ecological service types in the region to be evaluated.

[0027] According to the set initial priority weight, the corresponding service value S k of each ecological service in the grid cell is calculated and normalized, that is,

[0028]

[0029] , wherein S i,k represents the kth service value corresponding to the ith grid cell, R i,k represents the normalized value, S k represents the corresponding service value of the kth ecological service in the grid cell, max and min represent the maximum and minimum functions respectively.

[0030] Based on the normalized value, the entropy value corresponding to each service is calculated, that is,

[0031]

[0032] , wherein R i,k represents the normalized value, g M,N represents the total number of grid cells, M represents the upper limit of the serial number of grid cells, and H k represents the entropy value corresponding to the kth service.

[0033] According to the entropy value corresponding to the ecological service, the weight coefficient of the corresponding service is adjusted, that is,

[0034]

[0035] , wherein H k represents the entropy value corresponding to the kth service, K represents the upper limit of the number of ecological service types in the region to be evaluated, and w k represents the weight corresponding to the kth service.

[0036] As a preferred scheme of the farmland ecosystem service evaluation method based on remote sensing monitoring, the evaluation of the farmland ecological service is implemented as follows:

[0037] A standard threshold S of the comprehensive service value is set T , the farmland ecosystem service is evaluated according to a comparison result between the comprehensive service value S total and the comprehensive service standard threshold, and then,

[0038] If the comparison result satisfies the formula S total ≥ S T , it indicates that the farmland ecosystem service of the current region is qualified.

[0039] If the comparison result satisfies the formula S total < S T , it indicates that the farmland ecosystem service of the current region is unqualified.

[0040] As a preferred scheme of the farmland ecosystem service evaluation method based on remote sensing monitoring, the data correlation analysis between the ecological services is implemented as follows:

[0041] According to the data correlation analysis in the grid level, the type of the synergistic effect between the services is determined, and the determination is implemented as follows:

[0042]

[0043] wherein, cov(S k ,S m ) represents the covariance data between the kth service and the mth service, respectively represent the standard deviation of the kth service and the mth service, and ρ k,m represents the correlation coefficient between the kth service and the mth service, which is used to determine the type of the synergistic effect between the service functions, and the determination is implemented as follows:

[0044] If the calculated correlation coefficient satisfies the formula ρ k,m > 0, it indicates that the type of the synergistic effect between the two service functions is positive synergistic effect.

[0045] If the calculated correlation coefficient satisfies the formula ρ k,m < 0, it indicates that the type of the synergistic effect between the two service functions is positive-negative synergistic effect.

[0046] If the calculated correlation coefficient satisfies the formula ρ k,m = 0, it indicates that the type of the synergistic effect between the two service functions is no synergistic effect.

[0047] As a preferred scheme of the farmland ecosystem service evaluation method based on remote sensing monitoring, the synergistic effect coefficient between the two service functions is determined as follows:

[0048] C k,m = ρ k,m ·(1+α k,m )

[0049] wherein, ρ k,m represents the correlation coefficient between the kth service and the mth service, C k,m represents the calculated synergistic effect coefficient between the two services, α k,m represents a correction coefficient between services, which is determined by the type of synergistic effect between services, and is specifically:

[0050] If the type of synergistic effect between the two services is positive synergistic effect, the correction coefficient satisfies the formula α k,m > 0.

[0051] If the type of synergistic effect between the two services is negative synergistic effect, the correction coefficient satisfies the formula α k,m < 0.

[0052] If the type of synergistic effect between the two services is no synergistic effect, the correction coefficient satisfies the formula α k,m = 0.

[0053] As a preferred scheme of the farmland ecosystem service evaluation method based on remote sensing monitoring, the intelligent optimization of the farmland ecological service evaluation model is specifically as follows:

[0054] A weight coefficient target optimization function is constructed, and then,

[0055]

[0056] The function is constrained by setting constraint conditions, and specifically:

[0057] w k > 0

[0058]

[0059] wherein, S k represents the service value of the kth ecological service in the grid cell, K represents the upper limit of the number of ecological service types in the evaluation area, w k represents the weight coefficient of the kth ecological service, w m represents the weight coefficient of the mth ecological service, C k,m represents the calculated synergistic effect coefficient between the two services, and f(w k) represents a weight coefficient target optimization function, which is used to maximize the function by adjusting the synergy effect coefficient between services, so as to realize intelligent optimization of the cultivated land ecosystem service evaluation model.

[0060] As a preferred scheme of the cultivated land ecosystem service evaluation method based on remote sensing monitoring, wherein the optimization based on service deviation is specifically as follows:

[0061] The weight coefficient w of the input function maximization k , and the comprehensive service value S under the current weight coefficient is calculated t ′ otal , and the service value S of the kth ecological service in the grid unit k ′

[0062] The actual value S of the comprehensive service obtained in the field investigation actual ;

[0063] The comprehensive deviation and the deviation of the single service between the two are calculated, that is,

[0064] D=S t ′ -S otal -S actual

[0065] ΔS k =S k ′-S k

[0066] Wherein, S t ′ otal represents the comprehensive service value in the target function maximization, S actual represents the actual value of the comprehensive service obtained in the field investigation, S k ′ represents the service value of the kth ecological service in the grid unit in the target function maximization, S k represents the service value of the kth ecological service in the grid unit, D represents the calculated comprehensive deviation, and ΔS k represents the calculated deviation of the single service;

[0067] The comprehensive deviation threshold D T and the single service deviation threshold ΔS k,T are set, and whether the set weight coefficient needs to be dynamically adjusted is judged, which is specifically:

[0068] If the calculated comprehensive deviation and the single service deviation satisfy the formula D≥D T and ΔS k ≥ΔS k,T compared with the set threshold, it indicates that the calculated deviation exceeds the deviation threshold, and the weight distribution needs to be dynamically adjusted again, which is specifically:

[0069] According to the calculated deviation dynamically adjusting the weight distribution, then,

[0070] Based on the single service deviation dynamically adjusting the initial priority weight, then,

[0071]

[0072] Based on the comprehensive deviation dynamically adjusting the synergistic effect coefficient, then,

[0073]

[0074] Wherein, p k Indicates the initial priority weight of the kth ecological service, Indicates the adjusted initial priority weight of the kth ecological service, ΔS k Indicates the calculated single service deviation, C k,m Indicates the synergistic effect coefficient between two services, Indicates the adjusted synergistic effect coefficient between two services, D indicates the calculated comprehensive deviation;

[0075] For the adjusted initial priority weight and the synergistic effect coefficient, the comprehensive service value S t ′ otal Is recalculated;

[0076] And the new comprehensive deviation D' and the single service deviation ΔS k ′ Are calculated;

[0077] If the newly calculated comprehensive deviation and single service deviation satisfy the formula D' < D T And ΔS k ′ < ΔS k,T , then the calculated deviation does not exceed the deviation threshold, the dynamic adjustment of the weight coefficient is stopped, and the intelligent optimization of the cultivated land ecological service evaluation model is completed.

[0078] A computer device comprising a memory and a processor, the memory stores a computer program, and the processor realizes the steps of the cultivated land ecological system service evaluation method based on remote sensing monitoring when executing the computer program.

[0079] A computer readable storage medium, which stores a computer program, the computer program realizes the steps of the cultivated land ecological system service evaluation method based on remote sensing monitoring when executed by a processor.

[0080] The beneficial effects of the present application are:

[0081] The application can reduce redundant feature interference by collecting multi-dimensional cultivated land data through remote sensing technology and combining with sparsity constraint for dimension reduction processing, and provide a high-quality data basis for subsequent ecological service evaluation.

[0082] Based on the collaborative relationship analysis between data, the collaborative effect coefficient and weight dynamic distribution technology are used to significantly improve the intelligence and adaptability of cultivated land ecosystem service evaluation.

[0083] Combined with the function function and weight optimization method, the quantitative comprehensive evaluation of soil conservation, water conservation, carbon sequestration and other services is realized, and the scientificity and universality of the evaluation model are enhanced.

[0084] Through on-site monitoring feedback and comparison with model output results, deviation correction and parameter optimization are carried out to ensure that the evaluation results of the model are closer to the actual demand. BRIEF DESCRIPTION OF DRAWINGS

[0085] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. 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. Among them:

[0086] Figure 1 The overall method step structure diagram of the cultivated land ecosystem service evaluation method based on remote sensing monitoring of the present application is shown. DETAILED DESCRIPTION

[0087] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the 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 should be within the scope of protection of the present application.

[0088] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0089] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0090] The present application is described in detail in conjunction with the schematic diagram, and in the detailed description of the embodiments of the present application, the cross-sectional view of the device structure is partially enlarged without the general proportion for the convenience of illustration, and the schematic diagram is only an example, which should not limit the scope of protection of the present application herein. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in actual production.

[0091] Meanwhile, in the description of the present application, it should be noted that the terms "first, second or third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0092] Unless otherwise specifically defined and limited, the terms "mounting, connecting, connecting" in the present application should be understood broadly, for example: it can be fixed connection, detachable connection or integral connection; it can also be mechanical connection, electrical connection or direct connection, it can also be indirectly connected through intermediate medium, or it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0093] Embodiment 1

[0094] Reference Figure 1 For an embodiment of the present application, a farmland ecosystem service evaluation method based on remote sensing monitoring is provided, comprising the following steps,

[0095] S1: Remote sensing data acquisition and feature processing.

[0096] Specifically, the remote sensing data acquisition and feature processing is to obtain multi-dimensional farmland data of the to-be-detected area by satellite remote sensing technology, and to process the collected multi-dimensional farmland data for providing accurate data basis for subsequent farmland ecological service evaluation, which is specifically implemented as follows:

[0097] The multi-dimensional farmland data of the to-be-detected area is collected by satellite remote sensing technology, including surface coverage type data, vegetation index data and soil moisture data, the surface coverage type data is to extract spectral characteristic values of different objects at different wave bands according to spectral reflection characteristics of surface objects, and to combine the extracted spectral characteristic values and spatial distribution to classify and label each pixel to generate a surface coverage type distribution map; and the collected multi-dimensional farmland data is constructed as a multi-dimensional farmland data set X to provide data basis for subsequent data processing.

[0098] Further, the data processing of the collected multi-dimensional farmland data is to perform data dimension reduction on the constructed multi-dimensional farmland data set, and at the same time, introduce sparsity constraint to ensure that the features after data dimension reduction can not only maintain key information but also reduce the interference of redundant features, which is specifically implemented as follows:

[0099] Based on the constructed multi-dimensional cultivated land data set, it is divided into n sample sets, and based on the divided sample set, the corresponding covariance matrix is constructed, that is,

[0100]

[0101] Wherein, X i represents the divided i-th sample, μ represents the mean of the multi-dimensional cultivated land data set, n represents the upper limit of the number of divided samples, i represents the sample index of the division, T represents the transpose matrix, and C represents the calculated covariance matrix;

[0102] The multi-dimensional cultivated land data set is reduced and reconstructed, and there is,

[0103] According to the calculated covariance matrix, the multi-dimensional cultivated land data set is subjected to eigenvalue decomposition, and there is,

[0104] C=Q∧Q T

[0105] Wherein, C represents the calculated covariance matrix, T represents the transpose matrix, Q represents the eigenvector matrix, which is composed of a plurality of eigenvectors;

[0106] The original multi-dimensional cultivated land data set X is projected into the reduced dimension space, and there is,

[0107] Y=X·W

[0108] Wherein, Y represents the multi-dimensional cultivated land data set after dimension reduction, and W represents the projection matrix;

[0109] The sparsity constraint is introduced to ensure that the relevant features of cultivated land ecological service evaluation are retained in the reduced data, and there is,

[0110]

[0111] Wherein, represents the reconstruction error, and the sparsity constraint of data reconstruction is realized by error minimization, ||W||1 represents the sparsity regularization term, L represents the introduced sparsity constraint, and is used to ensure that the relevant features of cultivated land ecological service evaluation are retained in the reduced data.

[0112] It should be noted that by reducing and reconstructing the collected data, while introducing the sparsity constraint, the data can be reduced while ensuring that the subsequent cultivated land ecological service evaluation related features are retained, and the reliability of subsequent cultivated land ecological service evaluation is enhanced.

[0113] S2: Realize cultivated land ecological service evaluation according to the collected data.

[0114] Specifically, the cultivated land ecological service evaluation is achieved by building a cultivated land ecological service evaluation model, taking the collected cultivated land data as input data of the model, and achieving the cultivated land ecological service evaluation according to the output result of the model.

[0115] Further, the cultivated land ecological service evaluation model is based on spatial analysis and quantitative estimation of the cultivated land ecological service system to evaluate the comprehensive evaluation ability of different regions in soil conservation, water conservation, carbon sequestration, habitat maintenance and wind and sand prevention.

[0116] For the region to be evaluated, the region to be evaluated is divided into a two-dimensional grid network, the grid unit is g, and each grid unit has different ecological properties, including vegetation index, soil moisture and slope data, the ecological properties are diversified, and in the embodiment, they are not listed one by one, then,

[0117] G={g i,j |i=1,2,3,...,M;j=1,2,3,...,N}

[0118] Wherein, G represents the two-dimensional grid network divided by the region to be evaluated, M and N represent the rows and columns corresponding to the grid network respectively, i and j represent the sequence number indexes of the rows and columns of the grid units in the grid network;

[0119] According to different cultivated land ecological services, a function function f k (g i,j ) is set, and the service value of each service in the grid unit is calculated according to the set function function, then,

[0120]

[0121] Wherein, f k (g i,j ) represents the function function corresponding to the kth ecological service, M and N represent the rows and columns corresponding to the grid network respectively, i and j represent the grid units in the grid network, and S k represents the service value of the kth ecological service in the grid unit.

[0122] According to the demand of the region to be evaluated, the corresponding weight coefficient w k is allocated to different ecological services, and the comprehensive service value is calculated, then,

[0123]

[0124] Wherein, S k represents the service value of the kth ecological service in the grid unit, K represents the upper limit of the number of ecological services in the region to be evaluated, and w kWeight coefficient representing different ecological service distribution, S total Indicates the calculated comprehensive service value.

[0125] It should be noted that for different ecological services corresponding to the function function, the expression of the function is different according to different ecological services, as follows:

[0126] For soil conservation service, the function function is:

[0127] f k (g i,j ) = (R·K·LS·O·P)-(K·LS·O·P)

[0128] Wherein, R represents rainfall erosivity factor, K represents soil erodibility factor, LS represents slope length factor, O represents vegetation coverage factor, P represents soil protection measures factor;

[0129] For water conservation service, the function function is:

[0130] f k (g i,j ) = N P -AET-N Q

[0131] Wherein, N P represents rainfall, AET represents actual evapotranspiration, N Q represents surface runoff;

[0132] For carbon sequestration service, the function function is:

[0133] f k (g i,j ) = C veg +C soil

[0134] Wherein, C veg represents vegetation carbon storage, C soil represents soil carbon storage;

[0135] For habitat maintenance service, the function function is:

[0136]

[0137] Wherein, z represents the comprehensive influence value of landscape fragmentation and habitat threat factor;

[0138] For wind prevention and sand fixation service, the function function is:

[0139] f k (g i,j ) = NDVI·W s

[0140] wherein, NDVI represents a vegetation index, W s represents a wind speed weight factor;

[0141] For the expression of the function function, different services have different embodiments, and in the embodiment, only water source conservation service, soil conservation service, carbon sequestration service, habitat maintenance service and wind prevention and sand fixation service are described, which are not all the function expressions. In actual application, different expressions are selected according to different ecological services by the implementer, and the embodiment is not described in detail.

[0142] It should be noted that, in order to comprehensively evaluate the farmland ecological services of different regions, the distribution rules of the ecological service weight coefficients are formulated to realize the dynamic and scientific distribution of the weight coefficients and improve the comprehensiveness of the farmland ecological service evaluation. The specific distribution rules are as follows:

[0143] According to the demand of the region to be evaluated, an initial priority weight p k of each ecological service is allocated, the value range is [0, 1], and the formula is satisfied:

[0144]

[0145] wherein, p k represents the initial priority weight of the kth ecological service, and K represents the upper limit of the number of ecological services in the region to be evaluated;

[0146] According to the set initial priority weight, the corresponding service value S k of each ecological service in the grid unit is calculated, and normalized processing is performed, that is,

[0147]

[0148] wherein, S i,k represents the kth service value corresponding to the i th grid unit, R i,k represents the normalized value, S k represents the corresponding service value of the kth ecological service in the grid unit, max and min represent the maximum function and the minimum function respectively;

[0149] Based on the normalized value, the entropy value corresponding to each service is calculated, that is,

[0150]

[0151] wherein, R i,k represents the normalized value, g M,N represents the total number of grid units, M represents the upper limit of the serial number of the grid unit, and H k represents the entropy value corresponding to the kth service;

[0152] According to the entropy value corresponding to the ecological service, the weight coefficient of the corresponding service is adjusted, that is,

[0153]

[0154] wherein H k represents the entropy value corresponding to the kth service, K represents the upper limit of the number of ecological service types in the region to be evaluated, w k represents the weight corresponding to the kth service.

[0155] Further, the implementation of the cultivated land ecological service evaluation is based on the calculated comprehensive service value for evaluation, and the specific evaluation is as follows:

[0156] Set the comprehensive service value standard threshold S T , according to the comparison result between the comprehensive service value S total and the comprehensive service standard threshold, the cultivated land ecological system service evaluation is carried out, that is,

[0157] If the comparison result satisfies the formula S total ≥ S T , it indicates that the current region corresponds to the qualified cultivated land ecological system service;

[0158] If the comparison result satisfies the formula S total < S T , it indicates that the current region corresponds to the unqualified cultivated land ecological system service.

[0159] S3: Intelligent optimization based on the synergistic relationship between data.

[0160] Specifically, the intelligent optimization based on the synergistic relationship between data is to intelligently optimize the cultivated land ecological service evaluation model according to the synergistic relationship between the ecological system services in the region to be evaluated, so as to improve the accuracy of the model.

[0161] Further, the synergistic relationship between the ecological system services is determined according to the data correlation analysis between the ecological services, the synergistic effect coefficient between two service functions is determined, and the cultivated land ecological service evaluation model is intelligently optimized according to the determined synergistic effect coefficient, and the specific implementation is as follows:

[0162] Set the synergistic effect type between two services, including positive synergistic effect, negative synergistic effect and no synergistic effect, the positive synergistic effect is that the improvement of one service can promote another service (such as the increase of vegetation coverage helps carbon sequestration and water conservation), the negative synergistic effect is that the improvement of one service has negative impact on another service (such as excessive cultivation will cause the decrease of habitat maintenance), and the no synergistic effect is that there is no direct relationship between two services;

[0163] According to the data correlation analysis in the grid level, the synergy effect type between services is determined, specifically as follows:

[0164]

[0165] wherein cov(S k ,S m ) represents the covariance data between the kth service and the mth service, respectively represent the standard deviation of the kth service and the mth service, and p k,m represents the correlation coefficient between the kth service and the mth service, which is used to determine the synergy effect type between service functions, specifically as follows:

[0166] If the calculated correlation coefficient satisfies the formula p k,m > 0, it indicates that the synergy effect type between the two service functions is positive synergy effect.

[0167] If the calculated correlation coefficient satisfies the formula p k,m < 0, it indicates that the synergy effect type between the two service functions is positive-negative synergy effect.

[0168] If the calculated correlation coefficient satisfies the formula p k,m = 0, it indicates that the synergy effect type between the two service functions is no synergy effect.

[0169] According to the determined synergy effect type, the synergy effect coefficient between the two service functions is calculated, specifically as follows:

[0170] C k,m = p k,m · (1 + a k,m )

[0171] wherein p k,m represents the correlation coefficient between the kth service and the mth service, C k,m represents the calculated synergy effect coefficient between the two services, and a k,m represents the correction coefficient between services, which is determined by the synergy effect type between services, specifically as follows:

[0172] If the synergy effect type between the two services is positive synergy effect, the correction coefficient satisfies the formula a k,m > 0, and the specific value is set by the implementer according to the actual application scenario.

[0173] If the synergy effect type between the two services is negative synergy effect, the correction coefficient satisfies the formula a k,m < 0, and the specific value is set by the implementer according to the actual application scenario.

[0174] If the synergy type between the two services is no synergy, the correction coefficient value satisfies the formula α. k,m =0.

[0175] Furthermore, the intelligent optimization of the farmland ecosystem service assessment model involves constructing a weighted coefficient objective optimization function based on the calculated synergistic effect coefficients. By optimizing the ecosystem service weighted coefficients, the intelligent optimization of the farmland ecosystem service assessment model is achieved, specifically as follows:

[0176] Constructing the objective function for the weighting coefficients, we have:

[0177]

[0178] And set constraints to constrain the function, specifically:

[0179] w k >0

[0180]

[0181] Among them, S k w represents the service value of the k-th ecosystem service in the raster cell, where K represents the upper limit of the number of ecosystem service types in the area to be evaluated. k w represents the weighting coefficient for the allocation of the k-th type of ecosystem service. m C represents the weighting coefficient for the allocation of the m-th type of ecosystem service. k,m f(w) represents the synergy coefficient between the two services being calculated. k The weighting coefficient objective function is used to maximize the function by adjusting the synergistic effect coefficients among services, thereby achieving intelligent optimization of the farmland ecosystem service assessment model. Specifically:

[0182] The weight coefficient w when maximizing the input function k And calculate the comprehensive service value S under the current weight coefficient. t ′ otal And the service value S of the k-th ecological service in the raster cell. k ′;

[0183] Collect the actual value S of integrated services obtained from field surveys actual ;

[0184] Calculating the overall deviation between the two and the deviation of each individual service, we have:

[0185] D = S t ′ otal -S actual

[0186] ΔS k =S k′-S k

[0187] wherein S t ′ otal represents the comprehensive service value at the maximization of the objective function, S actual represents the actual value of the comprehensive service obtained in the field survey, S k ′ represents the service value of the kth ecological service in the grid cell at the maximization of the objective function, S k represents the service value of the kth ecological service in the grid cell, D represents the calculated comprehensive deviation, AS k represents the calculated deviation of the single service;

[0188] The comprehensive deviation threshold D T and the single service deviation threshold AS k,T is determined whether the set weight coefficient needs to be dynamically adjusted, specifically:

[0189] If the calculated comprehensive deviation and single service deviation satisfy the formula D≥D T and AS k ≥AS k,T , it indicates that the calculated deviation exceeds the deviation threshold, and the weight distribution needs to be dynamically adjusted again, specifically:

[0190] The weight distribution is dynamically adjusted according to the calculated deviation, that is,

[0191] The initial priority weight of the single service is dynamically adjusted based on the deviation, that is,

[0192]

[0193] The synergistic effect coefficient is dynamically adjusted based on the comprehensive deviation, that is,

[0194]

[0195] wherein p k represents the initial priority weight of the kth ecological service, represents the adjusted initial priority weight of the kth ecological service, AS k represents the calculated deviation of the single service, C k,m represents the synergistic effect coefficient between the two services, represents the adjusted synergistic effect coefficient between the two services, D represents the calculated comprehensive deviation;

[0196] For the adjusted initial priority weight and synergistic effect coefficient, the comprehensive service value S t ′ otal is recalculated;

[0197] and a new comprehensive deviation D' and a single service deviation AS are calculated k

[0198] If the newly calculated comprehensive deviation and single service deviation satisfy the formula D' < D T and AS k < AS k,T , it indicates that the calculated deviation does not exceed the deviation threshold, the dynamic adjustment of the weight coefficient is stopped, and the intelligent optimization of the farmland ecological service evaluation model is completed.

[0199] Further, the functions can be stored in a computer-readable storage medium if the functions are implemented in the form of software function units and sold or used as independent products. Based on this understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art, or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, etc.

[0200] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer-readable medium for use by an instruction execution system, apparatus or device, such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, apparatus or device and execute the instructions, or in conjunction with these instruction execution systems, apparatus or devices. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by an instruction execution system, apparatus or device, or in conjunction with these instruction execution systems, apparatus or devices.

[0201] ​More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM), and the like. Additionally, the computer-readable media can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and then stored in a computer memory in a form that is then reproducible into a computer readable medium.

[0202] In addition, for purposes of brevity of description, and not by way of limitation, the description is being made in the context of a single processor computer system. However, one of ordinary skill in the art will recognize that the teachings of this disclosure are equally applicable in the context of a multi-processor computer system, and that the disclosed embodiments can be implemented in the context of such a system.

[0203] It is to be understood that the development of the exemplary embodiments of this application can not be limited to the particular implementation described above, which are intended as one example of implementation and are presented for illustrative purposes only. Numerous variations are possible, as will be appreciated by those of ordinary skill in the art.

[0204] It should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not limit the present application, although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced, without departing from the spirit and scope of the technical solutions of the present application, and all should be included in the scope of the claims of the present application.

Claims

1. A method for assessing farmland ecosystem services based on remote sensing monitoring, characterized by: The process includes the following steps: using remote sensing technology to acquire multidimensional farmland data of the area to be evaluated, performing dimensionality reduction on the collected data, and introducing sparsity constraints to reduce interference from redundant features in the dataset. The assessment of farmland ecosystem services is conducted based on the collected data, specifically as follows: A farmland ecosystem service assessment model is built based on spatial analysis and quantitative estimation of the farmland ecosystem service system. Farmland data is used as input data, and the farmland ecosystem service is assessed based on the model's output. This also includes... When comprehensively evaluating the ecosystem services of arable land in different regions, rules for allocating ecosystem service weight coefficients should be formulated to achieve dynamic allocation of weight coefficients. Intelligent optimization is based on the collaborative relationships between data, specifically: Based on the data correlation analysis between ecosystem services, the synergy effect coefficient between the two service functions is determined, and the farmland ecosystem service assessment model is intelligently optimized based on the determined synergy effect coefficient. The optimization is judged as complete based on the service deviation. The specific details of establishing the farmland ecosystem service assessment model are as follows: The area to be evaluated is divided into a two-dimensional grid network, with the grid unit being... Furthermore, each grid cell has different ecological attributes, including vegetation index, soil moisture, and slope data, thus... in, This represents a two-dimensional raster network divided by the region to be evaluated. as well as These represent the rows and columns corresponding to the raster network, respectively. as well as Represents the row and column sequence index of a raster cell in a raster network; A corresponding function is set up based on the different farmland ecological services. And based on the set function, calculate the service value of each service in the raster cell, then we have, in, Indicates the first Functional functions corresponding to various ecosystem services. as well as These represent the rows and columns corresponding to the raster network, respectively. as well as Represents the raster cell in a raster network. Indicates the first The service value of a type of ecological service in a grid cell; Based on the needs of the area to be evaluated, corresponding weight coefficients are assigned to different ecosystem services. And by calculating the comprehensive service value, we have: in, Indicates the first The service value of a type of ecological service within a grid cell. This indicates the upper limit of the number of ecosystem service types in the area to be assessed. Represents the weighting coefficients for different ecosystem service allocations. This represents the calculated overall service value; The specific rules for allocating the ecosystem service weight coefficients are as follows; Initial priority weights are assigned to each ecosystem service based on the needs of the area to be evaluated. The numerical range is And satisfy the formula: in, Indicates the first The initial priority weights of various ecosystem services This indicates the upper limit of the number of ecosystem service types in the area to be assessed; Based on the set initial priority weights, calculate the corresponding service value of each ecological service in the grid cell. After normalization, we have: in, Indicates the first The corresponding raster cell is the first This type of service value This represents the normalized value. Indicates the first The corresponding service value of each ecological service in the raster cell. These represent the maximum value function and the minimum value function, respectively. Based on the normalized values, the entropy value corresponding to each service is calculated, and then we have: in, This represents the normalized value. Indicates the total number of grid cells. This indicates the upper limit of the sequence number of the raster cell. Indicates the first The entropy value corresponding to the service; Based on the entropy value corresponding to the ecosystem service, the weight coefficient of the corresponding service is adjusted, then we have: in, Indicates the first The entropy value corresponding to the service This indicates the upper limit of the number of ecosystem service types in the area to be assessed. Indicates the first The weight corresponding to each service; The data correlation analysis among the aforementioned ecosystem services is as follows: Based on data correlation analysis at the raster level, the types of synergistic effects between services are determined, as follows: in, Indicates the first Type of service and the first Covariance data between the services They represent the first Type of service and the first The standard deviation of the service Indicates the first Type of service and the first The correlation coefficient between services is used to determine the type of synergy between service functions, specifically: If the calculated correlation coefficient satisfies the formula This indicates that the synergy between the two service functions is a positive synergy. If the calculated correlation coefficient satisfies the formula This indicates that the synergy between the two service functions is a positive or negative synergy. If the calculated correlation coefficient satisfies the formula This indicates that the synergy type between the two service functions is no synergy. The specific steps for determining the synergy coefficient between the two service functions are as follows: in, Indicates the first Type of service and the first The correlation coefficient between the services This represents the synergy coefficient calculated between the two services. This represents the adjustment coefficient between services, determined by the type of synergy between services, specifically: If the synergy between the two services is a positive synergy, the correction coefficient should satisfy the formula. ; If the synergy between the two services is a negative synergy, the correction coefficient should satisfy the formula. ; If the synergy type between the two services is no synergy, the correction coefficient value satisfies the formula. ; The intelligent optimization of the farmland ecosystem service assessment model is as follows: Constructing the objective function for the weighting coefficients, we have: And set constraints to constrain the function, specifically: in, Indicates the first The service value of a type of ecological service within a grid cell. This indicates the upper limit of the number of ecosystem service types in the area to be assessed. Indicates the first Weighting coefficients for the allocation of ecosystem services. Indicates the first Weighting coefficients for the allocation of ecosystem services. This represents the synergy coefficient calculated between the two services. This represents the objective function for the weighting coefficients; The specific determination of whether optimization based on service deviation is complete is as follows: Weight coefficients when maximizing the input function And calculate the comprehensive service value under the current weighting coefficient. and the Service value of various ecological services in grid cells ; Collect actual values ​​of integrated services obtained from field surveys ; Calculating the overall deviation between the two and the deviation of each individual service, we have: in, This represents the overall service value when the objective function is maximized. This represents the actual value of the integrated services obtained during the field survey. When the objective function is maximized, the first... The service value of a type of ecological service within a grid cell. Indicates the first The service value of a type of ecological service within a grid cell. This represents the overall deviation in the calculation. Indicates the deviation of the calculated individual service; Set the overall deviation threshold and individual service deviation threshold To determine whether the set weighting coefficients need to be dynamically adjusted, the following steps are taken: If the calculated overall deviation and individual service deviation satisfy the set threshold, then... and This indicates that the calculation deviation exceeds the deviation threshold, and the weight allocation needs to be dynamically readjusted. Specifically: If the weight allocation is dynamically adjusted based on the calculated deviation, then the initial priority weights can be dynamically adjusted based on the deviation of a single service. Based on the dynamic adjustment of the synergistic effect coefficient according to the comprehensive deviation, we have: in, Indicates the first The initial priority weights of various ecosystem services Indicates the adjusted number The initial priority weights of various ecosystem services This indicates the deviation of the calculated individual service. This represents the synergy coefficient between the two services. This represents the synergy coefficient between the two services after adjustment. Indicates the overall deviation of the calculation; The overall service value is recalculated based on the adjusted initial priority weights and synergy coefficients. ; And calculate the new overall deviation. and deviations in individual services ; If the newly calculated overall deviation and individual service deviation satisfy the formula and If the calculated deviation does not exceed the deviation threshold, the dynamic adjustment of the weight coefficients will stop, and the intelligent optimization of the farmland ecosystem service assessment model will be completed.

2. The method for assessing farmland ecosystem services based on remote sensing monitoring as described in claim 1, characterized in that: The specific implementation of the farmland ecosystem service assessment is as follows: Set a standard threshold for comprehensive service value According to the comprehensive service value The comparison results between the farmland ecosystem service assessment and the comprehensive service standard threshold yield the following: If the comparison results satisfy the formula This indicates that the corresponding farmland ecosystem services in the current area are qualified; If the comparison results satisfy the formula This indicates that the ecosystem services of the arable land in the current area are inadequate.

3. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 2.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 2.

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