CN114841490A - Ecological protection priority area identification method, system, device and storage medium - Google Patents
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Abstract
The invention discloses a method, a system, a device and a storage medium for identifying an ecological protection priority area, wherein the method comprises the following steps: s1, constructing an ecological protection importance evaluation index system; s2, establishing an evaluation index space database; s3, performing principal component analysis of the evaluation index; s4, simulating an ecological protection importance evaluation scene based on OWA; and S5, recognizing ecological protection priority areas under different scenes and selecting the ecological protection priority areas preferentially. According to the invention, an ecological protection importance evaluation index system is constructed by coupling various indexes of the ecological system service and the ecological vulnerability, then the principle component method is used for determining the criterion weight, and the OWA algorithm is used for determining the bit sequence weight to simulate the comprehensive evaluation result of different risk situations, so that the problem that the relative importance and decision risk among various indexes of the ecological system service and the ecological vulnerability are ignored in the prior art for comprehensive evaluation is effectively avoided. The invention can be widely applied to the technical field of ecological planning and management.
Description
Technical Field
The invention relates to the technical field of ecological planning and management, in particular to a method, a system and a device for identifying an ecological protection priority area and a storage medium.
Background
The ecological protection priority area is an ecological protection key area which is defined according to the natural conditions and social and economic conditions of the area and aims to protect natural resources and guarantee the ecological safety of the area, and is beneficial to maintaining the ecological background stability, relieving the natural capital degradation pressure and improving the service function of an ecological system. Aiming at the problem of ecological protection vacancy which continuously occurs in the regional development process and the complexity of an ecological system, the ecological protection priority region identification method applied in the current urban ecological management process needs continuous and deep research. Therefore, exploring how to scientifically identify the ecological protection priority area has important significance for improving the urban ecological management decision level and promoting the cooperative symbiosis of people and nature.
Ecological system services and ecological vulnerability are brought into ecological environment evaluation for ecological protection priority area identification, and regional ecological safety can be comprehensively guaranteed from two aspects of internal stability and external functionality of the ecological system, namely maintaining the stability of the ecological system and guaranteeing the continuous supply of the ecological system services. Most researches on ecological environment evaluation only select indexes from a single aspect of ecological system service or ecological vulnerability or respectively select indexes from the ecological system service and the ecological vulnerability, and finally, the evaluation results on the two sides are simply superposed, so that the comprehensive consideration on the relative importance and weight between the ecological system service indexes and the ecological vulnerability indexes is lacked. Meanwhile, most ecological environment evaluation researches lack consideration of various decision risks, and the OWA (Ordered Weighted average) multi-attribute decision method is a method for providing a series of multi-attribute evaluation sets under the decision risks for decision makers according to different decision risk coefficients and by aggregating criterion weights and bit sequence weights, and is suitable for urban ecological management decisions such as ecological environment evaluation and the like needing to consider the decision risks by sequencing or preferentially selecting limited evaluation results based on a certain rule. Currently, the evaluation of the ecological environment is carried out by combining with OWA and then the evaluation result is used for the research of identification of the ecological protection priority area, on one hand, only the index of the service level of the ecological system is considered, but not the comprehensive adoption of the service and ecological vulnerability of the ecological system, on the other hand, the criterion weight is generally determined by using a subjective weighting method (such as an analytic hierarchy process), and the technical research of combining an objective weighting method (such as a principal component method) is lacked. One of the deficiencies of the existing ecological protection priority area identification technology is that the research of an OWA multi-attribute decision mechanism combining objective weighting and coupling ecosystem service and ecological vulnerability is lacked.
Disclosure of Invention
To solve at least one of the technical problems in the prior art to some extent, an object of the present invention is to provide a method, a system, a device and a storage medium for identifying an ecological protection priority area.
The technical scheme adopted by the invention is as follows:
an ecological protection priority area identification method comprises the following steps:
acquiring ecological system service factors and ecological vulnerability factors, and constructing an ecological protection importance evaluation index system;
establishing an evaluation index space database according to the ecological protection importance evaluation index system;
performing principal component analysis according to the evaluation index spatial database, determining principal component indexes based on ecological protection importance evaluation indexes, and determining criterion weights of the principal component indexes;
according to the OWA multi-attribute decision method, z decision risk coefficient scenes are set, the bit sequence weight of the principal component index is calculated, according to the bit sequence weight, the criterion weight and the principal component index data, OWA multi-attribute evaluation is carried out, and ecological protection importance space evaluation results under the z scenes are obtained;
and acquiring ecological protection priority regions under various scenes according to the ecological protection importance space evaluation result, calculating the protection efficiency of different scenes, and taking the ecological protection priority region under the scene with the optimal protection efficiency as an ecological protection priority region identification result of the research region based on OWA multi-attribute decision.
Further, the construction of the evaluation index system of the ecological protection importance comprises the following steps:
selecting indexes to construct an ecological protection importance evaluation index system which takes ecological protection importance evaluation as a target layer, takes ecological system service and ecological vulnerability as factor layers, takes supply service, regulation service and support service belonging to the ecological system service and exposure, sensitivity and adaptability belonging to the ecological vulnerability as criterion layers, and divides the selected indexes into positive indexes, negative indexes and qualitative indexes according to the influence on the target layer.
Further, the establishing of an evaluation index spatial database according to the ecological protection importance evaluation index system includes:
determining an evaluation method based on the data characteristics of each index according to the ecological protection importance evaluation index;
based on the data availability and the inherent difference between the indexes, collecting a corresponding data set according to an evaluation method;
and specifically evaluating each index according to an evaluation method, uniformly standardizing evaluation results, and setting the value range of the standardized results to be 0-1.
Further, the evaluation results are uniformly standardized, and the method comprises the following steps:
adopting a first preset formula to carry out the polar difference method standardization on the positive indexes, and adopting a second preset formula to carry out the polar difference method standardization on the positive indexes;
carrying out standardization on qualitative indexes by classification assignment, and carrying out classification assignment according to different land use types and index characteristics;
wherein, the expression of the first preset formula is as follows:
the expression of the second predetermined formula is:
in the formula, X norm Is the normalized index pixel value, X x Is an index pixel value, X max Is the maximum value of all pixel values of the index, X min Is the minimum value of all pixel values of the index.
Further, the performing principal component analysis according to the evaluation index spatial database, determining principal component indexes based on the ecological protection importance evaluation indexes, and determining criterion weights of the principal component indexes includes:
sampling raster image layers of all evaluation indexes to a point diagram layer converted from a raster with a resolution required by research by using a grid sampling tool of QGIS software, and then importing attribute table data of the point diagram layer into SPSS software for principal component analysis;
and performing KMO (KMO) test and Bartlett's test on the principal component analysis report, then taking the characteristic value greater than 1 as a criterion for selecting the principal component, finally determining the principal component index based on the ecological protection importance evaluation index, and determining the criterion weight of each principal component index with the sum of 1 according to the principal component variance contribution rate.
Further, the calculating the bit-order weight of the principal component index includes:
the bit sequence weight of the principal component index is calculated in an increasing mode by using a monotone rule, and the calculation formula is as follows:
wherein j is a bit sequence; v. of j Is bit sequence weight, v j ∈[0,1](ii) a n is the number of main component indexes; alpha is a decision risk coefficient, alpha belongs to (0, infinity); w is a k The importance level is the main component index; r is k And assigning the main component index, assigning the index according to the size of the index value, assigning a maximum value of 1, a secondary maximum value of 2 and a minimum value of n.
Further, the acquiring of the ecological protection priority regions under various scenes according to the ecological protection importance space evaluation result, calculating the protection efficiency of different scenes, and taking the ecological protection priority region under the scene with the optimal protection efficiency as the ecological protection priority region identification result of the research region based on the OWA multi-attribute decision includes:
calculating the ratio of the accumulated ecological protection importance pixel value and the total ecological protection importance pixel value in descending order based on the ecological protection importance space evaluation result under z scenes, and taking the ecological protection importance pixel value corresponding to the ratio of 50% as the ecological protection importance threshold value of each scene;
using a polymerization tool in ArcGIS software, polymerizing relatively aggregated or adjacent pattern spots in a region which is greater than or equal to an ecological protection importance threshold into relatively complete connected pattern spots, wherein the polymerization distance is the resolution required by research, and the polymerization result is used as a primary ecological protection priority area of each scene;
to reduce fragmentation of the ecological protection priority zone, scenario z is utilized 1 Determining the area threshold of the pattern spots needing to be eliminated according to the primary ecological protection priority area pattern spots; wherein, the scene z 1 The scenes with equal sequence weight of each index are obtained;
statistical analysis of scene z 1 Taking the minimum area threshold corresponding to the inflection point as the final area threshold of the image spots, and removing corresponding independent dispersed small image spots according to the threshold to obtain the final ecological protection priority area of each scene;
comparing final ecological protection priority zones for different subjective decision scenarios to scenario z 1 The protection efficiency of the ecological protection importance evaluation result is obtained, and the final ecological protection priority area of the scene with the optimal protection efficiency is used as an ecological protection priority area identification result of the research area based on OWA multi-attribute decision;
the calculation method of the protection efficiency comprises the following steps:
wherein z is a scene number, z is 1,2,. z, and Pz is the protection efficiency of the scene z,
situation z in the area of the ecological protection priority zone as situation z 1 The average value of the importance of ecological protection of (c),
for the whole area of investigation region within the scene z 1 Average value of ecological protection importance of.
The other technical scheme adopted by the invention is as follows:
an ecological protection priority zone identification system comprising:
the index system construction module is used for acquiring an ecological system service factor and an ecological vulnerability factor and constructing an ecological protection importance evaluation index system;
the database construction module is used for establishing an evaluation index space database according to the ecological protection importance evaluation index system;
the index analysis module is used for performing principal component analysis according to the evaluation index spatial database, determining principal component indexes based on ecological protection importance evaluation indexes, and determining the criterion weight of each principal component index;
the scene evaluation module is used for setting z decision risk coefficient scenes according to the OWA multi-attribute decision method, calculating the bit sequence weight of the principal component index, and performing OWA multi-attribute evaluation according to the bit sequence weight, the criterion weight and the principal component index data to obtain ecological protection importance space evaluation results under the z scenes;
and the scene screening module is used for acquiring ecological protection priority regions under various scenes according to the ecological protection importance space evaluation result, calculating the protection efficiency of different scenes, and taking the ecological protection priority region with the scene with the optimal protection efficiency as an ecological protection priority region identification result of the research region based on OWA multi-attribute decision.
The other technical scheme adopted by the invention is as follows:
an ecological protection priority area recognition device comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method described above.
The other technical scheme adopted by the invention is as follows:
a computer readable storage medium in which a processor executable program is stored, which when executed by a processor is for performing the method as described above.
The beneficial effects of the invention are: according to the invention, an ecological protection importance evaluation index system is constructed by coupling various indexes of the ecological system service and the ecological vulnerability, then the principle component method is used for determining the criterion weight, and the OWA algorithm is used for determining the bit sequence weight to simulate the comprehensive evaluation result of different risk situations, so that the problem that the relative importance and decision risk among various indexes of the ecological system service and the ecological vulnerability are ignored in the prior art for comprehensive evaluation is effectively avoided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description is made on the drawings of the embodiments of the present invention or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of an ecological protection priority area identification method according to an embodiment of the present invention;
FIG. 2 is a spatial distribution map of evaluation indexes according to an embodiment of the present invention;
FIG. 3 is a graph illustrating the statistical analysis of the ratio of the number of the patches with an area smaller than the threshold to all the patches and the total area of the patches with an area smaller than the threshold according to the area threshold in step five in the embodiment of the present invention;
fig. 4 is a spatial distribution map of the ecological protection priority area of each scenario in the embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
As shown in fig. 1, the present embodiment provides an ecological protection priority area identification method based on OWA multi-attribute decision, which includes the following steps:
s1, constructing an ecological protection importance evaluation index system.
Based on the characteristics of an ecological system in a research area, main influence factors of the ecological environment, core ecological problems and related literature induction and policy interpretation, indexes are selected to construct an ecological protection importance evaluation index system which takes ecological protection importance evaluation as a target layer, takes ecological system service and ecological vulnerability as a factor layer, takes supply service, regulation service and support service belonging to the ecological system service and exposure, sensitivity and adaptability belonging to the ecological vulnerability as a criterion layer, and divides the selected indexes into positive indexes, negative indexes and qualitative indexes according to the influence on the target layer.
The three criteria layer indexes of the supply service, the regulation service and the support service belong to an ecological system service factor layer, and the three criteria layer indexes of the exposure degree, the sensitivity and the adaptability belong to an ecological vulnerability factor layer.
And S2, establishing an evaluation index space database.
And determining an evaluation method based on the data characteristics of each index according to the ecological protection importance evaluation index selected in the step S1. Based on the data availability and the inherent difference between each index, a corresponding data set is collected according to an evaluation method, then software such as an InVEST model and ArcGIS is used for specifically evaluating each index according to the evaluation method, the evaluation results are standardized uniformly, and the standardized result value range is 0-1. The standardization processing method specifically comprises the following steps:
s11, respectively adopting formula 1 and formula 2 to carry out the polar difference method standardization on the positive and negative indexes:
the forward direction index is as follows:
negative direction index:
wherein, X norm Is the normalized index pixel value, X x Is an index pixel value, X max Is the maximum value of all pixel values of the index, X min Is the minimum value of all pixel values of the index.
And S12, carrying out standardization on the qualitative indexes by adopting classification assignment, and carrying out classification assignment according to different land use types and index characteristics.
And S3, performing principal component analysis of the evaluation index.
Based on the evaluation index obtained in step S2, using a grid sampling tool of the QGIS software, all evaluation index raster layer data are sampled to a point map layer converted from a grid of a resolution required for the study, and then attribute table data of the point map layer are imported to the SPSS software for principal component analysis. And performing KMO (KMO) test and Bartlett's test on the principal component analysis report, then taking the characteristic value greater than 1 as a criterion for selecting the principal component, finally determining the principal component index based on the ecological protection importance evaluation index, and determining the criterion weight of each principal component index with the sum of 1 according to the principal component variance contribution rate.
In some optional embodiments, the KMO test and Bartlett's test are specifically performed by: if the KMO sampling adequacy quantity reported by the principal component analysis is more than 0.7, and the significance of the Butterworth sphericity test is less than 0.001, the evaluation index data is suitable for principal component extraction through the KMO test and the Bartlett's test; otherwise, judging that the evaluation index data do not pass the KMO test and the Bartlett's test, and is not suitable for extracting the main components.
And S4, simulating the ecological protection importance evaluation scene based on the OWA.
According to the OWA multi-attribute decision method, z (z is more than or equal to 5) decision risk coefficient scenes are set, then a monotone Rule Increment (RIM) method is used for calculating the bit sequence weight of the principal component indexes obtained based on the step S3, and the specific calculation method is shown as a formula 3 and a formula 4:
wherein j is a bit sequence; v. of j Is the weight of the bit sequence and is,v j ∈[0,1](ii) a n is the number of main component indexes; alpha is a decision risk coefficient, alpha belongs to (0, infinity); w is a k The importance level is the main component index; r is k And assigning the main component index, assigning the index according to the size of the index value, assigning a maximum value of 1, a secondary maximum value of 2 and a minimum value of n.
And performing OWA multi-attribute evaluation by using an MCE module of TerrSet software according to the previously obtained bit sequence weight, the standard weight obtained in the step S3 and the principal component index data. According to the evaluation method, the ecological protection importance space evaluation result under z situations is obtained through aggregation of the bit sequence weight and the criterion weight. Let the scenario with equal index sequence weight be scenario z 1 The situation evaluation result is equivalent to that the decision risk coefficient representing the subjective decision attitude is not considered, namely, the principle weight obtained by an objective weighting method-the principle component method is only considered for carrying out principle component index aggregation.
And S5, recognizing ecological protection priority areas under different scenes and selecting the ecological protection priority areas preferentially.
And based on the z kinds of ecological protection importance space evaluation results obtained in step S4, calculating a ratio of the accumulated ecological protection importance pixel value in descending order arrangement to the total ecological protection importance pixel value, and taking the ecological protection importance pixel value corresponding to the ratio of 50% as the ecological protection importance threshold value of each scene. Then using a polymerization tool in ArcGIS software to polymerize relatively aggregated or adjacent patches in a region which is greater than or equal to an ecological protection importance threshold into relatively complete connected patches, wherein the polymerization distance is the resolution required by research, and the minimum hole size is 1km 2 And taking the polymerization result as a primary ecological protection priority area of each scene.
To reduce fragmentation of the ecological protection priority zone, scenario z is utilized 1 And determining the area threshold of the pattern spots needing to be removed according to the primary ecological protection priority area pattern spots. Statistical analysis of scene z 1 The ratio of the number of the pattern spots with the area smaller than the threshold value in the initial ecological protection priority area to all the pattern spots and the condition that the total area of the pattern spots changes along with the area threshold value are adopted, the minimum area threshold value corresponding to the inflection point is taken as the final pattern spot area threshold value, corresponding independent dispersed small pattern spots are removed according to the threshold value,and obtaining the final ecological protection priority area of each scene. Then comparing the final ecological protection priority areas of different subjective decision scenes (scene 1 to scene z) with respect to the scene z 1 The protection efficiency of the ecological protection importance evaluation result is obtained, and therefore the situation with the optimal protection efficiency is screened out. The calculation method of the protection efficiency is shown in formula 5:
wherein z is a scene number, and z is 1,2 z For the efficiency of the protection of the scenario z,
situation z in the area of the ecological protection priority zone as situation z 1 The average value of the importance of ecological protection of (c),
for the whole area of investigation region within the scene z 1 Average value of ecological protection importance of. Based on the analysis, the final ecological protection priority area with the optimal protection efficiency scene is used as an ecological protection priority area identification result of the research area based on the OWA multi-attribute decision.
As can be seen from the above, in the method for identifying an ecological protection priority area based on OWA multi-attribute decision provided in this embodiment, an ecological protection importance evaluation system is first constructed by coupling an ecosystem service and an ecological vulnerability factor, and then, specific evaluation and standardization processing of an index layer are performed by using software such as an InVEST model and ArcGIS and multi-source data, so as to establish an evaluation index space database. And then, performing principal component analysis of the evaluation index based on the SPSS platform, generating principal components and determining the criterion weight of the principal components. On the basis, an OWA (Ordered Weighted average) multi-attribute decision method is utilized, and ecological protection importance evaluation situations under different decision risks are simulated by setting different decision risk coefficients and corresponding bit sequence weights. And aggregating the regions which are more than or equal to the ecological protection importance threshold in the evaluation result, and then removing the image spots which are less than the area threshold to obtain the final ecological protection priority region of each scene. And finally, screening out the optimal ecological protection priority area scene based on the protection efficiency, namely the ecological protection priority area identification result based on the OWA multi-attribute decision. The method is scientific, universal and reproducible in steps, is suitable for quantitative evaluation of ecological protection importance and ecological protection priority area identification under a specific time frame in any area, and provides theoretical basis and technical support for ecological protection zoning decision and ecological management.
The method is explained in detail with reference to specific embodiments below, in which a research area is selected as a yue hong and ao major bay area, and the method for identifying an ecological protection priority area based on OWA multi-attribute decision includes the following steps:
step one, constructing an ecological protection importance evaluation index system.
Based on the characteristics of an ecological system in a research area, main influence factors of the ecological environment, core ecological problems and relevant literature induction and policy interpretation, 17 indexes are selected to construct an ecological protection importance evaluation index system which takes ecological protection importance evaluation as a target layer, ecological system service and ecological vulnerability as a factor layer, supply service, regulation service and support service belonging to the ecological system service and exposure, sensitivity and adaptability belonging to the ecological vulnerability as a criterion layer, and the selected indexes are divided into positive indexes, negative indexes and qualitative indexes according to the influence on the target layer. The ecological protection importance evaluation index system is shown in table 1;
TABLE 1 evaluation index System for ecological protection importance
And step two, establishing an evaluation index space database.
And determining an evaluation method based on the data characteristics of each index according to the ecological protection importance evaluation index selected in the step one, as shown in table 2. Based on the data availability and the inherent difference between each index, corresponding data sets are collected according to an evaluation method, as shown in table 4, then software such as an InVEST model and ArcGIS are used for specifically evaluating each index according to the evaluation method, the evaluation results are standardized uniformly, and the standardized result value range is 0-1. The evaluation index spatial distribution map is shown in fig. 2. The standardization processing method specifically comprises the following steps:
(1) the positive and negative indexes are respectively normalized by a range method by adopting a formula 1 and a formula 2:
the forward direction index is as follows:
negative direction index:
wherein, X norm Is the normalized index pixel value, X x Is an index pixel value, X max Is the maximum value of all pixel values of the index, X min Is the minimum value of all pixel values of the index.
(2) The qualitative indexes are standardized by classification assignment, and are classified and assigned according to different land use types and index characteristics, which are detailed in table 5.
Table 2 index and method for evaluating ecological protection importance of Bay district in Guangdong, hong Kong and Australia
Table 3 reachability classification assignment table of degeneration source
TABLE 4 data set for evaluating ecological protection importance indices
Note: all data are resampled or spatially interpolated to 100m spatial resolution by ArcGIS software
TABLE 5 qualitative index standardization classification assignment table
And step three, performing principal component analysis of the evaluation index.
Based on the 17 standardized evaluation indexes obtained in the step two, a grid sampling tool of QGIS software is used for sampling all the evaluation index grid layers to point graph layers which are formed by grid conversion of 100m resolution, and then attribute table data of the point graph layers are imported into SPSS software for principal component analysis. KMO test and Bartlett's test are carried out on the principal component analysis report, then the characteristic value is larger than 1 and is taken as the standard for selecting the principal component, finally 5 principal component indexes based on the ecological protection importance evaluation index are determined, and the criterion weight of each principal component index with the sum of 1 is determined according to the principal component variance contribution rate, which is detailed in Table 6.
TABLE 6 characteristic values of principal component indexes, variance contribution ratios and criterion weights
Note: the principal component analysis report of this example shows: the KMO sampling fitness number is 0.813 (> 0.7) and the significance of the Butterworth sphericity test is 0.000 (< 0.001), so the data of this example were subjected to KMO test and Bartlett's test, and the extraction of the main component was suitably performed.
And fourthly, simulating the ecological protection importance evaluation scene based on the OWA.
According to the OWA multi-attribute decision method, 9 decision risk coefficient scenarios (α ═ 0.00001, 0.1, 0.2, 0.5, 1,2, 5, 10, 100000) are set, and then bit-order weights based on the 5 principal component indexes obtained in step three are calculated by using a monotone Rule Increment (RIM) method, wherein the specific calculation method is shown in formulas 3 and 4:
wherein j is a bit sequence; v. of j Is bit sequence weight, v j ∈[0,1](ii) a n is the number of main component indexes; alpha is a decision risk coefficient, alpha belongs to (0, infinity); w is a k The importance level is the main component index; r is k And assigning the main component index, assigning the index according to the size of the index value, assigning a maximum value of 1, a secondary maximum value of 2 and a minimum value of n. The decision risk coefficients and bit-sequence weights for the 9 scenarios are shown in table 7.
And performing OWA multi-attribute evaluation by using an MCE module of TerrSet software according to the bit sequence weight obtained in the previous step, the criterion weight obtained in the step three and the principal component index data. According to the evaluation method, ecological protection importance space evaluation results under 9 scenes are obtained by aggregating the bit sequence weight and the criterion weight. The indexes of the scene 5 are equal in sequence weight, and the evaluation result is equivalent to that the decision risk coefficient representing the subjective decision attitude is not considered, namely, the principle weight obtained by an objective weighting method-a principle component method is only considered for carrying out principle component index aggregation.
TABLE 7 decision Risk coefficients and bit-order weights under different scenarios
And fifthly, identifying and preferentially selecting the ecological protection priority areas under different scenes.
And calculating the ratio of the accumulated ecological protection importance pixel value to the total ecological protection importance pixel value in descending order based on the 9 ecological protection importance space evaluation results obtained in the fourth step, and taking the ecological protection importance pixel value corresponding to the ratio of 50% as the ecological protection importance threshold value of each scene (see table 8 for details). Then relatively aggregated or adjacent patches in the area which is greater than or equal to the threshold value of ecological protection importance are aggregated into relatively complete connected patches by using an aggregation tool in ArcGIS software, wherein the aggregation distance is 100m, and the minimum hole size is 1km 2 And taking the polymerization result as a primary ecological protection priority area of each scene.
In order to reduce fragmentation of the ecological protection priority area, the area threshold of the image spots needing to be removed is determined by using the initial ecological protection priority area image spots of the scene 5. Statistically analyzing the ratio of the number of the patches with the area smaller than the threshold in the preliminary ecological protection priority area of the scenario 5 to the number of all the patches and the change of the total area along with the area threshold, see fig. 3 in detail, in this embodiment, the minimum area threshold corresponding to the inflection point of fig. 3 is 24km 2 As the final image spot area threshold, and according to the threshold, eliminating the corresponding independent dispersed small image spots to obtain the final ecological protection priority area of each scene, as shown in fig. 4. And comparing the protection efficiencies of the final ecological protection priority areas of different subjective decision scenes (scenes 1-9) to the ecological protection importance evaluation result of the scene 5, thereby screening out a scene with the optimal protection efficiency. The calculation method of the protection efficiency is shown in formula 5:
wherein z is a scene number, and z is 1,2 z For the efficiency of the protection of the scenario z,
the average value of the ecological protection importance of scenario 5 within the ecological protection priority zone of scenario z,
the average ecological preservation importance for scenario 5 across the entire area of study was about 0.4069. The average value of the ecological protection importance and the protection efficiency of scenario 5 in the scope of the ecological protection priority area of each scenario are detailed in table 8. Based on the above analysis, the final ecological protection priority region of scenario 4 is selected as the ecological protection priority region identification result of the research region based on the OWA multi-attribute decision.
TABLE 8 threshold value of ecological protection importance for different scenarios, average value of ecological protection importance for scenario 5 within ecological protection priority zone, and protection efficiency
In summary, compared with the prior art, the method of the embodiment has the following advantages and beneficial effects:
(1) the invention couples various indexes of the ecosystem service and the ecological vulnerability to construct an ecological protection importance evaluation index system, and then determines the criterion weight by using a principal component method. Meanwhile, the steps of the method are scientific, universal and reproducible, and the data processing and spatial analysis related to the process can be realized through InVEST, ArcGIS, SPSS and TerrSet software which are widely used in multiple fields of ecology, geography, planning and the like at present, so that the method is suitable for quantitative measurement of ecological protection importance of a research area and ecological protection priority area identification under a specific time frame in any area.
(2) The method is characterized in that a principle weight and a bit sequence weight are respectively determined by a principal component method (objective weighting method) and an OWA multi-attribute decision method (decision analysis method for simulating different subjective intentions of a decision maker), and ecological protection importance evaluation and an ecological protection priority area under different scenes are scientifically simulated by combining the principal and objective methods, so that the problem of excessive subjective components of decision results of the conventional ecological protection priority area identification technology combining the subjective weighting method and the OWA is avoided, visual and visual multi-scene results under different decision risk coefficients are provided for the decision maker, and the urban ecological management decision level is improved.
(3) The specific technical method for determining the ecological protection importance threshold value of the spatial range for extracting the ecological protection priority area from the whole research area and the area threshold value of the independent dispersed small image spots for reducing fragmentation and removal of the ecological protection priority area is provided, and meanwhile, the method for screening the optimal ecological protection priority area situation from multiple situations based on the optimal protection efficiency is innovatively provided, so that valuable technical guidance is provided for the decision of optimal natural resource management and ecological protection under multiple situations in ecological planning and management.
This embodiment also provides an ecological protection priority area identification system, includes:
the index system construction module is used for acquiring an ecological system service factor and an ecological vulnerability factor and constructing an ecological protection importance evaluation index system;
the database construction module is used for establishing an evaluation index space database according to the ecological protection importance evaluation index system;
the index analysis module is used for performing principal component analysis according to the evaluation index spatial database, determining principal component indexes based on ecological protection importance evaluation indexes, and determining the criterion weight of each principal component index;
the scene evaluation module is used for setting z decision risk coefficient scenes according to the OWA multi-attribute decision method, calculating the bit sequence weight of the principal component index, and performing OWA multi-attribute evaluation according to the bit sequence weight, the criterion weight and the principal component index data to obtain ecological protection importance space evaluation results under the z scenes;
and the scene screening module is used for acquiring ecological protection priority regions under various scenes according to the ecological protection importance space evaluation result, calculating the protection efficiency of different scenes, and taking the ecological protection priority region with the scene with the optimal protection efficiency as an ecological protection priority region identification result of the research region based on OWA multi-attribute decision.
The ecological protection priority area identification system of the embodiment can execute the ecological protection priority area identification method provided by the embodiment of the method of the invention, can execute any combination implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
This embodiment still provides an ecological protection priority district recognition device, includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of fig. 1.
The ecological protection priority area identification device of the embodiment can execute the ecological protection priority area identification method provided by the embodiment of the method of the invention, can execute any combination implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
Embodiments of the present application also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor to cause the computer device to perform the method illustrated in fig. 1.
The embodiment also provides a storage medium, which stores an instruction or a program capable of executing the ecological protection priority region identification method provided by the embodiment of the method of the invention, and when the instruction or the program is executed, the method can be executed by any combination of the embodiment of the method, and the method has corresponding functions and beneficial effects.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An ecological protection priority area identification method is characterized by comprising the following steps:
acquiring ecological system service factors and ecological vulnerability factors, and constructing an ecological protection importance evaluation index system;
establishing an evaluation index space database according to the ecological protection importance evaluation index system;
performing principal component analysis according to the evaluation index spatial database, determining principal component indexes based on ecological protection importance evaluation indexes, and determining criterion weights of the principal component indexes;
according to the OWA multi-attribute decision method, z decision risk coefficient scenes are set, the bit sequence weight of the principal component index is calculated, according to the bit sequence weight, the criterion weight and the principal component index data, OWA multi-attribute evaluation is carried out, and ecological protection importance space evaluation results under the z scenes are obtained;
and acquiring ecological protection priority areas under various scenes according to the ecological protection importance space evaluation result, calculating the protection efficiency of different scenes, and taking the ecological protection priority area with the scene with the optimal protection efficiency as an ecological protection priority area identification result of the research area based on OWA multi-attribute decision.
2. The ecological protection priority area identification method according to claim 1, wherein the constructing of an evaluation index system of ecological protection importance comprises:
selecting indexes to construct an ecological protection importance evaluation index system which takes ecological protection importance evaluation as a target layer, takes ecological system service and ecological vulnerability as factor layers, takes supply service, regulation service and support service belonging to the ecological system service and exposure, sensitivity and adaptability belonging to the ecological vulnerability as criterion layers, and divides the selected indexes into positive indexes, negative indexes and qualitative indexes according to the influence on the target layer.
3. The ecological protection priority area identification method according to claim 2, wherein the establishing of an evaluation index space database according to an ecological protection importance evaluation index system comprises:
determining an evaluation method based on the data characteristics of each index according to the ecological protection importance evaluation index;
based on the data availability and the inherent difference between the indexes, collecting a corresponding data set according to an evaluation method;
and specifically evaluating each index according to an evaluation method, uniformly standardizing evaluation results, and setting the value range of the standardized results to be 0-1.
4. The ecological protection priority area identification method according to claim 3, wherein the evaluation result is uniformly standardized, and the method comprises the following steps:
adopting a first preset formula to carry out the polar difference method standardization on the positive indexes, and adopting a second preset formula to carry out the polar difference method standardization on the positive indexes;
carrying out standardization on qualitative indexes by adopting classification assignment, and carrying out classification assignment according to different land utilization types and index characteristics; wherein, the expression of the first preset formula is as follows:
the expression of the second predetermined formula is:
in the formula, X norm Is the normalized index pixel value, X x Is an index pixel value, X max Is the maximum value of all pixel values of the index, X min Is the minimum value of all pixel values of the index.
5. The ecological protection priority region identification method according to claim 1, wherein the performing principal component analysis according to an evaluation index spatial database, determining principal component indexes based on ecological protection importance evaluation indexes, and determining criterion weights of the principal component indexes comprises:
sampling raster image layers of all evaluation indexes to a point diagram layer converted from a raster with a resolution required by research by using a grid sampling tool of QGIS software, and then importing attribute table data of the point diagram layer into SPSS software for principal component analysis; and performing KMO (KMO) test and Bartlett's test on the principal component analysis report, then taking the characteristic value greater than 1 as a criterion for selecting the principal component, finally determining the principal component index based on the ecological protection importance evaluation index, and determining the criterion weight of each principal component index with the sum of 1 according to the principal component variance contribution rate.
6. The ecological protection priority area identification method according to claim 1, wherein the calculating the bit-order weight of the principal component index comprises:
the bit sequence weight of the principal component index is calculated in an increasing mode by using a monotone rule, and the calculation formula is as follows:
wherein j is a bit sequence; v. of j Is bit sequence weight, v j ∈[0,1](ii) a n is the number of main component indexes; alpha is a decision risk coefficient, alpha belongs to (0, infinity); w is a k The importance level is the main component index; r is k And assigning the main component index, assigning the index according to the size of the index value, assigning a maximum value of 1, a secondary maximum value of 2 and a minimum value of n.
7. The ecological protection priority region identification method according to claim 1, wherein the steps of obtaining ecological protection priority regions under various scenes according to the ecological protection importance space evaluation result, calculating the protection efficiency of different scenes, and taking the ecological protection priority region with the optimal protection efficiency scene as the ecological protection priority region identification result of the research region based on the OWA multi-attribute decision comprise:
calculating the ratio of the accumulated ecological protection importance pixel value and the total ecological protection importance pixel value in descending order based on the ecological protection importance space evaluation result under z scenes, and taking the ecological protection importance pixel value corresponding to the ratio of 50% as the ecological protection importance threshold value of each scene;
using a polymerization tool in ArcGIS software, polymerizing relatively aggregated or adjacent pattern spots in a region which is greater than or equal to an ecological protection importance threshold into relatively complete continuous pattern spots, wherein the polymerization distance is the resolution required by research, and the polymerization result is used as a primary ecological protection priority region of each scene;
to reduce fragmentation of the ecological protection priority zone, scenario z is utilized 1 Determining the area threshold of the pattern spots needing to be eliminated according to the primary ecological protection priority area pattern spots; wherein, the scene z 1 The scenes with equal sequence weight of each index are obtained;
statistical analysis of scene z 1 Taking the minimum area threshold corresponding to the inflection point as the final area threshold of the image spots, and removing corresponding independent dispersed small image spots according to the threshold to obtain the final ecological protection priority area of each scene;
comparing final ecological protection priority zones for different subjective decision scenarios to scenario z 1 The protection efficiency of the ecological protection importance evaluation result is obtained, and the final ecological protection priority area of the scene with the optimal protection efficiency is used as an ecological protection priority area identification result of the research area based on OWA multi-attribute decision;
the calculation method of the protection efficiency comprises the following steps:
wherein z is a scene number, and z is 1,2 z For the efficiency of the protection of the scenario z,
situation z in the area of the ecological protection priority zone as situation z 1 The average value of the importance of ecological protection of (c),
for the whole area of investigation region within the scene z 1 Average value of ecological protection importance of.
8. An ecological protection priority area identification system, comprising:
the index system construction module is used for acquiring an ecological system service factor and an ecological vulnerability factor and constructing an ecological protection importance evaluation index system;
the database construction module is used for establishing an evaluation index space database according to the ecological protection importance evaluation index system;
the index analysis module is used for performing principal component analysis according to the evaluation index spatial database, determining principal component indexes based on ecological protection importance evaluation indexes, and determining the criterion weight of each principal component index;
the scene evaluation module is used for setting z decision risk coefficient scenes according to the OWA multi-attribute decision method, calculating the bit sequence weight of the principal component index, and performing OWA multi-attribute evaluation according to the bit sequence weight, the criterion weight and the principal component index data to obtain ecological protection importance space evaluation results under the z scenes;
and the scene screening module is used for acquiring ecological protection priority regions under various scenes according to the ecological protection importance space evaluation result, calculating the protection efficiency of different scenes, and taking the ecological protection priority region with the scene with the optimal protection efficiency as an ecological protection priority region identification result of the research region based on OWA multi-attribute decision.
9. An ecological protection priority area recognition device, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, in which a program executable by a processor is stored, wherein the program executable by the processor is adapted to perform the method according to any one of claims 1 to 7 when executed by the processor.
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