A method, device and equipment for determining habitat suitability during the breeding period of harbor seals

Abstract

The present invention provides a method, device and equipment for determining the suitability of the habitat of spotted seals during the breeding period, which relates to the field of ecology. The method for determining the suitability of the habitat of spotted seals during the breeding period includes: obtaining sea ice thickness data of the entire target area; obtaining multiple target landscape pattern indexes through the sea ice thickness data; obtaining sea ice thickness suitability index through the sea ice thickness data; obtaining a comprehensive index based on multiple target landscape pattern indexes and the sea ice thickness suitability index; determining the suitability of the habitat of spotted seals during the breeding period in the target area through the comprehensive index. The scheme of the present invention realizes the accurate evaluation of the comprehensive suitability of the habitat of spotted seals during the breeding period.

Description

Technical Field

[0001] The invention relates to the field of ecology, and in particular to a method, a device and equipment for determining the suitability of a habitat for a spotted seal during its breeding period. Background Art

[0002] At present, the industry has not yet established a mature and reliable method and process for evaluating the comprehensive suitability of the spotted seal's breeding habitat. In current technology, there is a lack of comprehensive indicators for the evaluation of the suitability of the spotted seal's breeding habitat, that is, there is a lack of a comprehensive index to quantify the changes in the spotted seal's breeding habitat. The density, thickness, and area of ​​sea ice will have a certain impact on the reproduction of spotted seals, but in-depth analysis of the impact of sea ice changes on spotted seals is required. The analysis results based solely on traditional sea ice indicators are poor and cannot effectively reflect the impact of key factors such as sea ice pattern and sea ice thickness on its breeding conditions, thereby making it impossible to accurately evaluate the comprehensive suitability of the spotted seal's breeding habitat. Summary of the invention

[0003] The present invention provides a method, device and equipment for determining the suitability of the habitat during the breeding period of spotted seals, which solves the problem that the traditional sea ice index analysis results cannot accurately evaluate the comprehensive suitability of the habitat during the breeding period of spotted seals.

[0004] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0005] An embodiment of the present invention provides a method for determining the suitability of a habitat for a harbor seal during its breeding period, comprising:

[0006] Obtain sea ice thickness data for the entire target area;

[0007] Through sea ice thickness data, multiple target landscape pattern indices are obtained;

[0008] Obtain sea ice thickness suitability index through sea ice thickness data;

[0009] Obtaining a comprehensive index based on the plurality of target landscape pattern indices and the sea ice thickness suitability index;

[0010] The comprehensive index is used to determine the suitability of the habitat for harbor seals in the target area during the breeding period.

[0011] Optionally, multiple target landscape pattern indices can be obtained through sea ice thickness data, including:

[0012] The sea ice thickness data is classified and processed by a reclassification method to obtain a first sea ice classification result;

[0013] Based on the target area, the first sea ice classification result is clipped within a preset range to obtain a second sea ice classification result of a preset number of ice periods per day in the target area;

[0014] Through a preset program, the second sea ice classification result is used to calculate indicators and obtain multiple initial landscape pattern indices;

[0015] The multiple initial landscape pattern indices are screened to obtain multiple target landscape pattern indices.

[0016] Optionally, a plurality of the initial landscape pattern indices are screened to obtain a plurality of target landscape pattern indices, including:

[0017] By formula The correlation between the multiple initial landscape pattern indexes is calculated to obtain the correlation data between the multiple initial landscape pattern indexes; wherein A is the first initial landscape pattern index, B is the second initial landscape pattern index, r AB is the Pearson correlation coefficient, which is used to measure the linear correlation between two variables A and B. AB ∈[-1,1], l is the total number of samples of variables A and B, i is the current variable, A i is the i-th element of variable A, B i is the i-th element of variable B, is the average value of the elements in A, is the average value of the elements in B;

[0018] According to the correlation data, the multiple initial landscape pattern indices are screened to obtain multiple target landscape pattern indices.

[0019] Optionally, obtain the sea ice thickness suitability index through sea ice thickness data, including:

[0020] Determine the range of sea ice thickness suitable for harbor seal breeding;

[0021] The sea ice thickness suitability index is obtained by counting the sea ice thickness data on the fishing net using a preset programming language and calculating the ratio of the sea ice area of ​​the sea ice thickness range data corresponding to the sea ice thickness data to the total sea ice area.

[0022] Optionally, obtaining a comprehensive index based on a plurality of the target landscape pattern indexes and the sea ice thickness suitability index includes:

[0023] Performing index preprocessing on the plurality of landscape pattern indices and the sea ice thickness suitability index by using the first preset formula or the second preset formula to obtain a plurality of transformation indices;

[0024] Among them, the first preset formula is The second preset formula is Where m represents any one of the landscape pattern index and sea ice thickness suitability index; X nm ′ are all values ​​after exponential preprocessing, X nm is the value of the original index, max X m and min X m Respectively represent the maximum and minimum values ​​of the current index m;

[0025] Based on multiple transformed indices, a comprehensive index is obtained.

[0026] Optionally, obtain a composite index based on multiple transformation indices, including:

[0027] Based on the transformation indexes, determining a correlation coefficient matrix R between the transformation indexes;

[0028] Based on the correlation coefficient matrix R between the transformed indices, obtaining the eigenvalues ​​of the correlation coefficient matrix R;

[0029] According to the eigenvalue of the correlation coefficient matrix R, the comprehensive index is obtained.

[0030] Optionally, a comprehensive index is obtained according to the eigenvalue of the correlation coefficient matrix R, including:

[0031] According to the eigenvalues ​​of the correlation coefficient matrix R, the number of principal components of the transformation index set and the variance contribution rate corresponding to each principal component are determined;

[0032] Determine eigenvectors based on eigenvalues;

[0033] According to the number of principal components and eigenvectors, the expressions of each principal component are determined;

[0034] The comprehensive index is obtained according to the variance contribution rate and principal component expression corresponding to each principal component.

[0035] The embodiment of the present invention further provides a device for determining the suitability of the habitat of harbor seals during their breeding period, comprising:

[0036] An acquisition module is used to obtain sea ice thickness data for the entire target area;

[0037] The processing module is used to obtain multiple target landscape pattern indices through sea ice thickness data; obtain a sea ice thickness suitability index through sea ice thickness data; obtain a comprehensive index based on the multiple target landscape pattern indices and the sea ice thickness suitability index; and determine the habitat suitability of the target area during the breeding period of spotted seals through the comprehensive index.

[0038] An embodiment of the present invention further provides a computing device, comprising: a processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method for determining the suitability of the habitat of the harbor seal during the breeding period as described above is executed.

[0039] An embodiment of the present invention further provides a computer-readable storage medium, comprising: storing instructions, which, when executed on a computer, enable the computer to execute the method for determining the suitability of the habitat of harbor seals during their breeding period as described above.

[0040] The above solution of the present invention includes at least the following beneficial effects:

[0041] The method for determining the habitat suitability of spotted seals during the breeding period of the present invention obtains sea ice thickness data of the entire target area; obtains multiple target landscape pattern indexes through the sea ice thickness data; obtains sea ice thickness suitability index through the sea ice thickness data; obtains a comprehensive index through a preset method based on the multiple target landscape pattern indexes and the sea ice thickness suitability index; and determines the habitat suitability of spotted seals during the breeding period in the target area through the comprehensive index. The method realizes an accurate assessment of the comprehensive suitability of the habitat of spotted seals during the breeding period. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic flow chart of a method for determining habitat suitability during the breeding period of harbor seals of the present invention;

[0043] Figure 2 It is a heat map of correlation coefficient of landscape pattern index in the method for determining habitat suitability during the breeding period of harbor seals of the present invention;

[0044] Figure 3 It is a module schematic diagram of a device for determining habitat suitability during the breeding period of harbor seals of the present invention. DETAILED DESCRIPTION

[0045] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0046] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a method for determining the suitability of a habitat for a harbor seal during its breeding period, comprising:

[0047] Step 11, obtaining sea ice thickness data for the entire target area;

[0048] Step 12, obtaining multiple target landscape pattern indexes through sea ice thickness data;

[0049] Step 13, obtaining a sea ice thickness suitability index through sea ice thickness data;

[0050] Step 14, obtaining a comprehensive index based on the plurality of target landscape pattern indexes and the sea ice thickness suitability index;

[0051] Step 15, determining the habitat suitability of harbor seals in the target area during the breeding period through the comprehensive index.

[0052] In this embodiment, sea ice in the entire target area can be identified, and then the identification results can be inverted to obtain sea ice thickness data; the inversion results of the sea ice thickness can be used to represent the analysis results of sea ice fragmentation; the method for determining the suitability of the habitat for spotted seals during the breeding period of the present invention analyzes the changes in the habitat of spotted seals by introducing landscape ecology landscape pattern indicators, and combines the sea ice thickness suitability index to provide a more comprehensive and in-depth method to evaluate the suitability of the habitat for spotted seals during the breeding period; the landscape pattern index can highly condense the landscape pattern information, and the tool for studying the structure and spatial pattern of natural landscapes can quantify and describe the spatial relationships and patterns between landscape elements; this innovation can highly condense the landscape pattern information, thereby more accurately revealing the impact of sea ice changes on spotted seals. The impact of sea ice thickness suitability index on the breeding conditions of spotted seals; through the comprehensive acquisition of landscape pattern index and sea ice thickness suitability index, a quantifiable habitat suitability index for the breeding period of spotted seals is formed; this index will be able to accurately and comprehensively measure the impact of sea ice fragmentation and its thickness on the breeding conditions of spotted seals, thereby providing scientists and researchers with a reliable tool for evaluating and monitoring changes in the habitat of spotted seals during the breeding period; the present invention realizes the accurate evaluation of the comprehensive suitability of the habitat of spotted seals during the breeding period, thereby being able to more accurately reveal the impact of sea ice changes on the breeding conditions of spotted seals; it can effectively solve the problem of lack of comprehensive index in the prior art, provide a scientific basis for in-depth understanding of the suitability of the habitat of spotted seals during the breeding period, and promote the development of related ecological environmental protection and management work.

[0053] In an optional embodiment of the present invention, step 12 may include:

[0054] Step 121, classifying the sea ice thickness data by a reclassification method to obtain a first sea ice classification result;

[0055] Step 122, based on the target area, the first sea ice classification result is clipped within a preset range to obtain a second sea ice classification result of a preset number of ice periods per day in the target area;

[0056] Step 123, calculating the index of the second sea ice classification result through a preset program to obtain a plurality of initial landscape pattern indexes;

[0057] Step 124, screening the multiple initial landscape pattern indices to obtain multiple target landscape pattern indices.

[0058] In this embodiment, the inversion result of sea ice thickness can be divided into two categories of water and sea ice (first sea ice classification result) by reclassification, and the first sea ice classification result is clipped by a preset range (i.e., the first sea ice classification result is clipped by a preset study area), and finally the second sea ice classification results of 11 ice periods in the target area are obtained, and then the second sea ice classification results are imported into the preset program Fragstats4.2 (landscape analysis software) software to calculate the index (a series of indicators of patch scale and patch type scale can be selected during calculation, and the calculation scale of landscape pattern index can be set to a range of 500m×500m), and multiple initial landscape pattern indexes are obtained; these results will provide an important data basis for subsequent studies on habitat changes of spotted seals. In order to accurately evaluate the quantitative impact of sea ice landscape pattern in the target area on the habitat of spotted seals, the selection of these indexes is intended to comprehensively evaluate the structural characteristics and fragmentation degree of sea ice landscape. The initial landscape pattern index can be selected from 17 commonly used landscape pattern indexes as shown in Table 1 for analysis.

[0059] Table 1. Commonly used landscape pattern index white table

[0060]

[0061] In an optional embodiment of the present invention, step 124 may include:

[0062] Step 1241, by formula The correlation between the multiple initial landscape pattern indexes is calculated to obtain the correlation data between the multiple initial landscape pattern indexes; wherein A is the first initial landscape pattern index, B is the second initial landscape pattern index, r AB is the Pearson correlation coefficient, which is used to measure the linear correlation between two variables A and B. AB ∈[-1,1], l is the total number of samples of variables A and B, i is the current variable, A i is the i-th element of variable A, B i is the i-th element of variable B, is the average value of the elements in A, is the average value of the elements in B;

[0063] Step 1242: Screen the multiple initial landscape pattern indices according to the correlation data to obtain multiple landscape pattern indices.

[0064] In this embodiment, when r AB >0, indicating that A and B are positively correlated; when r AB <0, indicating that A and B are negatively correlated; when r AB=0, indicating that A and B are not related; the larger the absolute value of the correlation coefficient, the stronger the correlation. The correlation degree of the variables is shown in Table 2, the comparison table of correlation coefficient and variable correlation degree;

[0065] Table 2. Comparison table of correlation coefficients and variable correlation

[0066] The absolute value range of the correlation coefficient Variable correlation 0.8～1.0 Very strong correlation 0.6～0.8 Strong correlation 0.4～0.6 Moderately strong correlation 0.2～0.4 Weak correlation 0.0～0.2 Weak or no correlation <0.0 Negative correlation

[0067] By analyzing the correlation strength between the initial landscape pattern indices, we can determine which landscape pattern indicators play a key role in describing landscape structure characteristics and ecological processes. Such analysis helps to screen out the most representative and meaningful indicators, thereby reducing redundant information; at the same time, it reveals the relationship between the initial landscape pattern indices, deeply understands the characteristics of the sea ice landscape structure, and infers their impact on the habitat of the spotted seal. Specifically, PCC (Pearson correlation coefficient) can be used to calculate the correlation between 17 commonly used landscape pattern indices (initial landscape pattern indices). For two highly correlated indices, only one of them is retained to participate in the principal component analysis experiment. The correlation results are as follows: Figure 2 The heat map of the correlation coefficient of landscape pattern index is shown. Figure 2 The calculation results show that the correlation between the NP (number of patches) index and the PD (patch density) index, as well as the TE (total length of boundaries) index and the ED (edge ​​density) index are all higher than 0.99, where NP is the number of patches, which is a simple description of landscape heterogeneity and fragmentation, and is a useful metric that can be used to show the amount of sea ice debris; PD is the patch density, which has the same meaning as the NP index when the landscape area is fixed; in addition, TE is the total edge length, and ED is the edge density, which is more useful than the total edge length because it describes the edge length based on unit area; therefore, only the NP index and the ED index can be retained in the two groups of indices to reduce redundant information. Through index screening, it was finally determined to remove TE and PD, thereby obtaining 15 landscape pattern indices other than TE and PD from the 17 initial landscape pattern indices as the target landscape pattern indices.

[0068] In an optional embodiment of the present invention, step 13 may include:

[0069] Step 131, determining the sea ice thickness range data suitable for harbor seal breeding;

[0070] Step 132, obtaining a sea ice thickness suitability index by counting the sea ice thickness data on a fishing net using a preset programming language and calculating a ratio of the sea ice area of ​​the sea ice thickness range data corresponding to the sea ice thickness data to the total sea ice area.

[0071] In this embodiment, in order to perform spatial analysis of sea ice thickness data, we can use a fishing net for symbolic representation. In ArcGIS software, we can create a fishing net by running the tools in ArcToolbox, select the Create Fishnet tool, set the output type to area pixels, use rectangular pixels to obtain the fishing net, set the height and width of the area pixels to 1000 meters, and then use a preset programming language to count the distribution of sea ice thickness data on the fishing net; specifically, first investigate the spotted seal data and know that the sea ice thickness range of 10 cm to 30 cm is the most suitable sea ice thickness for spotted seal breeding; therefore, first determine the sea ice thickness range data suitable for spotted seal breeding as 10 cm to 30 cm, and then use the Python programming language to count the sea ice thickness data on the fishing net, and calculate the ratio of the sea ice area with a thickness range of 10 cm to 30 cm to the total sea ice area to obtain the sea ice thickness suitability index P; wherein, Among them, A j is the area of ​​the jth pixel on the fishing net, A total is the total sea ice area, N is the total number of pixels on the fishing net, T j is the thickness of the jth pixel on the fishing net; Con j To determine the condition, determine whether the thickness of the j-th pixel on the fishing net is between 10 cm and 30 cm. If it meets the range, the area of ​​the pixel is included, otherwise it is not included.

[0072] In an optional embodiment of the present invention, step 14 may include:

[0073] Step 141, performing index preprocessing on the plurality of landscape pattern indices and the sea ice thickness suitability index by using the first preset formula or the second preset formula to obtain a plurality of transformation indices;

[0074] Among them, the first preset formula is The second preset formula is Where m represents any one of the landscape pattern index and sea ice thickness suitability index; X nm ′ are all values ​​after exponential preprocessing, X nm is the value of the original index, max X m and min X m Respectively represent the maximum and minimum values ​​of the current index m;

[0075] Step 142, obtaining a comprehensive index based on multiple transformation indices.

[0076] In this embodiment, index preprocessing is performed on 15 of the landscape pattern indices and 1 of the sea ice thickness suitability index to obtain 16 transformed indices; wherein, index preprocessing refers to eliminating dimensions through standardization, that is, dimensionless indexes; since the selected landscape indices include positive indices (the larger the index value, the greater the landscape fragmentation) and negative indices (the smaller the index value, the greater the landscape fragmentation), it is necessary to perform index preprocessing on the indices to achieve uniform changes; the specific positive and negative indices of the indices can refer to Table 1, and the present invention adopts the range method standardization to perform uniform transformation processing on the data, thereby achieving the uniformity of the data, wherein the index of the positive index is processed by the first preset formula, and the index of the negative index is processed by the second preset formula.

[0077] In an optional embodiment of the present invention, step 142 may include:

[0078] Step 1421, determining a correlation coefficient matrix R between transformation indices based on the transformation indices;

[0079] Step 1422, based on the correlation coefficient matrix R between the transformation indexes, calculating the eigenvalues ​​of the correlation coefficient matrix R;

[0080] Step 1423, obtaining a comprehensive index according to the eigenvalue of the correlation coefficient matrix R.

[0081] In this embodiment, step 1421 is specifically as follows: firstly, a transformation matrix is ​​established n times and the data matrix is ​​calculated as: Establish the correlation coefficient matrix R between the transformation indexes, R = (r ij ) p*p , p represents a total of p different transformation indices (that is, 16), r ij The definition is Represents the matrix X 1 , x ka Represents x 11 ; a, b represent the rows and columns of the matrix respectively; Taking the GOCI thickness inversion data in January, February and March 2013 as an example, the correlation coefficient matrix of 16 indices (transformed indices) is calculated as shown in Table 3 below (where r 11 =1,r 12 =0.002);

[0082] Table 3, Correlation Matrix

[0083]

[0084]

[0085]

[0086] In this embodiment, step 1422 is specifically as follows: According to the above correlation coefficient matrix R, R is a p-order symmetric matrix, and the characteristic root can be obtained by the characteristic equation of R | R-λ e E|=0 to obtain (λ e =λ 1 ,λ 2 ,…,λ P )in, The eigenvalues ​​of the 16 components are shown in Table 4 below;

[0087] Table 4, 16 components characteristic value statistics

[0088]

[0089] In an optional embodiment of the present invention, step 1423 may include:

[0090] Step 14231, determining the number of principal components of the transformation index set and the variance contribution rate corresponding to each principal component according to the eigenvalue of the correlation coefficient matrix R;

[0091] Step 14232, determining an eigenvector according to the eigenvalue;

[0092] Step 14233, determining the expressions of the principal components according to the number of principal components and the eigenvectors;

[0093] Step 14234, obtain the comprehensive index according to the variance contribution rate and the principal component expression corresponding to each principal component.

[0094] In this embodiment, the degree of landscape fragmentation is affected by various characteristics such as patch edge, patch size and connectivity, so the degree of landscape fragmentation cannot be represented by a single landscape pattern index. Similarly, the degree of sea ice fragmentation cannot be represented by randomly selecting one or several landscape pattern indexes in the table. Therefore, the present invention uses principal component analysis to calculate a comprehensive index based on the landscape pattern index and the suitable thickness ratio index, and uses the comprehensive index as the final representation of the habitat suitability of the spotted seal breeding period. The principal component analysis uses the idea of ​​mathematical dimensionality reduction to analyze the correlation between various indicators and convert multiple indicator problems into several principal components containing a lot of information to simplify the research problem. These small number of principal components not only contain a lot of information of the original indicators, but also the information between the principal components is unrelated. The principal component analysis method is a classic feature value selection and dimensionality reduction method, which is characterized by being able to weaken the error caused by the autocorrelation between variables, objectively determine the variable weights, and avoid subjective arbitrariness. The comprehensive index generated by the transformation is called the principal component, where each principal component is a linear combination of the original variables and the principal components are independent of each other, which makes the principal component have some superior performance than the original variables; one principal component is not enough to represent the original p variables, so it is necessary to find the second or even the third or fourth principal component. The second principal component should no longer contain the information of the first principal component. The statistical description is to make the covariance of the two principal components zero, and the geometric description is that the directions of the two principal components are orthogonal.

[0095] In this embodiment, the variance contribution rate reflects the role of each principal component in describing the evaluated object. The variance contribution rate is defined as λ e is the eigenvalue, and the number of principal components is determined according to the cumulative variance, which is defined as s represents the number of principal components; p represents the number of transformation indexes; the present invention takes the cumulative variance greater than 85% as the principal component. According to Table 4, three principal components can be extracted. The first three new variables are extracted as principal components, which explains 87.100% of the total variance, and the characteristic values ​​of the three principal components are λ 1 =6.953,λ 2 =5.638,λ 3 =1.345;

[0096] According to the eigenvalue, the eigenvector, i.e., the principal component loading matrix, is calculated. e The corresponding eigenvector U e From the equation system |R-λ e E|U e =0 to find; The results are shown in Table 5 below;

[0097] Table 5, Principal component loading matrix

[0098]

[0099] Finally, each principal component is calculated according to the principal component loading matrix, and the final comprehensive index is calculated according to the variance contribution rate of each principal component; as shown in Table 6, Table 6 is the expression and comprehensive index obtained by extracting three principal components according to the principal component loading matrix.

[0100]

[0101] Among them, Y represents the comprehensive index, Y 1 , Y 2 , Y 3 Represent the expressions of the three principal components, a 1 、a 2 、a 3 Respectively represent the variance contribution rate corresponding to each principal component, and the variance contribution rate corresponding to each principal component is defined by the variance contribution rate formula Get.

[0102] The present invention takes the breeding period of harbor seals from January to March 2013 as an example, and calculates the comprehensive suitability of the habitat during the breeding period of harbor seals through the above method. The present invention uses GOCI images to invert sea ice thickness data, and the sea ice thickness data can be obtained based on existing data sets or existing research results. The final fitness results of the breeding period of harbor seals in 2013 are as follows:

[0103] date Composite Index date Composite Index date Composite Index January 2 1.07 January 26 0.78 February 19 1.19 January 3 1.17 January 27 0.73 February 21 0.77 January 4 0.82 February 1 1.01 February 22 1.01 January 5 0.97 February 2 1.02 February 23 1.12 January 6 1 February 4 1.10 February 27 1.06 January 7 0.61 February 6 0.76 March 3 1.39 January 8 0.96 February 7 0.85 March 4 1.59 January 12 0.5 February 8 0.89 March 5 1.43 January 14 0.86 February 10 0.95 March 8 1.28 January 16 0.86 February 12 0.42 March 10 1.66 January 17 0.98 February 13 0.45 March 11 1.47 January 19 0.93 February 15 1.01 March 13 1.49 January 22 0.96 February 16 0.59 March 14 1.33 January 25 1.14 February 18 1.03 March 16 1.49

[0104] This method will form a new habitat suitability index for spotted seals during the breeding period through the comprehensive acquisition of landscape pattern index and sea ice thickness suitability index. Compared with existing technologies and research, it brings the following significant results: Comprehensive assessment of habitat suitability for spotted seals: Compared with traditional sea ice indicators, this method considers more factors, including sea ice fragmentation and sea ice thickness suitable for spotted seal breeding conditions, so as to more comprehensively and accurately assess the suitability of spotted seal habitats; Efficiently condense landscape information: The introduction of landscape pattern index can highly condense landscape information, effectively reflect the spatial relationship and pattern of sea ice, and provide a deeper understanding of changes in habitats during the breeding period of spotted seals; Quantify the impact of sea ice : Through the comprehensive acquisition of landscape pattern index and sea ice thickness suitability index, this method provides a quantitative method to measure the impact of sea ice changes on the breeding conditions of harbor seals, allowing researchers to analyze relevant data more accurately; Improve the depth of research: Compared with existing technologies, this method can analyze the changes in the habitat of harbor seals more deeply, providing scientists with more detailed information on the impact of sea ice on the breeding of harbor seals; To verify these effects, Fragstats software can be used to measure sea ice landscape indicators, and field survey data can be used to verify the index; Evaluation criteria can include correlation with the actual breeding situation of harbor seals, accuracy and stability of the model, etc. By comparing the experimental results, the superiority of this method over existing technologies can be clearly demonstrated.

[0105] like Figure 3 As shown, an embodiment of the present invention provides a device 20 for determining habitat suitability of harbor seals during breeding period, comprising:

[0106] An acquisition module is used to obtain sea ice thickness data for the entire target area;

[0107] The processing module is used to obtain multiple target landscape pattern indices through sea ice thickness data; obtain a sea ice thickness suitability index through sea ice thickness data; obtain a comprehensive index based on the multiple target landscape pattern indices and the sea ice thickness suitability index; and determine the habitat suitability of the target area during the breeding period of spotted seals through the comprehensive index.

[0108] Optionally, multiple target landscape pattern indices can be obtained through sea ice thickness data, including:

[0109] The sea ice thickness data is classified and processed by a reclassification method to obtain a first sea ice classification result;

[0110] Based on the target area, the first sea ice classification result is clipped within a preset range to obtain a second sea ice classification result of a preset number of ice periods per day in the target area;

[0111] Through a preset program, the second sea ice classification result is used to calculate indicators and obtain multiple initial landscape pattern indices;

[0112] The multiple initial landscape pattern indices are screened to obtain multiple target landscape pattern indices.

[0113] Optionally, a plurality of the initial landscape pattern indices are screened to obtain a plurality of target landscape pattern indices, including:

[0114] By formula The correlation between the multiple initial landscape pattern indexes is calculated to obtain the correlation data between the multiple initial landscape pattern indexes; wherein A is the first initial landscape pattern index, B is the second initial landscape pattern index, r AB is the Pearson correlation coefficient, which is used to measure the linear correlation between two variables A and B. AB ∈[-1,1], l is the total number of samples of variables A and B, i is the current variable, A i is the i-th element of variable A, B i is the i-th element of variable B, is the average value of the elements in A, is the average value of the elements in B;

[0115] According to the correlation data, the multiple initial landscape pattern indices are screened to obtain multiple target landscape pattern indices.

[0116] Optionally, obtain the sea ice thickness suitability index through sea ice thickness data, including:

[0117] Determine the range of sea ice thickness suitable for harbor seal breeding;

[0118] The sea ice thickness suitability index is obtained by counting the sea ice thickness data on the fishing net using a preset programming language and calculating the ratio of the sea ice area of ​​the sea ice thickness range data corresponding to the sea ice thickness data to the total sea ice area.

[0119] Optionally, obtaining a comprehensive index based on a plurality of the target landscape pattern indexes and the sea ice thickness suitability index includes:

[0120] Performing index preprocessing on the plurality of landscape pattern indices and the sea ice thickness suitability index by using the first preset formula or the second preset formula to obtain a plurality of transformation indices;

[0121] Among them, the first preset formula is The second preset formula is Where m represents any one of the landscape pattern index and sea ice thickness suitability index; X nm ′ are all values ​​after exponential preprocessing, X nm is the value of the original index, max Xm and min X m Respectively represent the maximum and minimum values ​​of the current index m;

[0122] Based on multiple transformed indices, a comprehensive index is obtained.

[0123] Optionally, obtain a composite index based on multiple transformation indices, including:

[0124] Based on the transformation indexes, determining a correlation coefficient matrix R between the transformation indexes;

[0125] Based on the correlation coefficient matrix R between the transformed indices, the eigenvalues ​​of the correlation coefficient matrix R are calculated;

[0126] According to the eigenvalue of the correlation coefficient matrix R, the comprehensive index is obtained.

[0127] Optionally, a comprehensive index is obtained according to the eigenvalue of the correlation coefficient matrix R, including:

[0128] According to the eigenvalues ​​of the correlation coefficient matrix R, the number of principal components of the transformation index set and the variance contribution rate corresponding to each principal component are determined;

[0129] Determine eigenvectors based on eigenvalues;

[0130] According to the number of principal components and eigenvectors, the expressions of each principal component are determined;

[0131] The comprehensive index is obtained according to the variance contribution rate and principal component expression corresponding to each principal component.

[0132] It should be noted that this device is a device corresponding to the above-mentioned method for determining the suitability of the habitat during the breeding period of spotted seals. All implementation methods in the above-mentioned method are applicable to the embodiments of this device and can achieve the same technical effects.

[0133] An embodiment of the present invention also provides a computing device, comprising: a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method for determining the suitability of the habitat of the harbor seal during the breeding period as described.

[0134] The embodiment of the present invention also provides a computer-readable storage medium, including instructions, which, when executed on a computer, enable the computer to execute the method for determining the habitat suitability of harbor seals during the breeding period as described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0135] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0136] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0137] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0138] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0139] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0140] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0141] In addition, it should be noted that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0142] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

[0143] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining the suitability of a habitat for harbor seals during their breeding period, characterized in that: include: Obtain sea ice thickness data for the entire target area; Through sea ice thickness data, multiple target landscape pattern indices are obtained; Obtain sea ice thickness suitability index through sea ice thickness data; Obtaining a comprehensive index based on the plurality of target landscape pattern indices and the sea ice thickness suitability index; Determine the habitat suitability of harbor seals during the breeding period in the target area through the comprehensive index; Among them, multiple target landscape pattern indices are obtained through sea ice thickness data, including: The sea ice thickness data is classified and processed by a reclassification method to obtain a first sea ice classification result; Based on the target area, the first sea ice classification result is clipped within a preset range to obtain a second sea ice classification result of a preset number of ice periods per day in the target area; Through a preset program, the second sea ice classification result is used to calculate indicators and obtain multiple initial landscape pattern indices; Screening the multiple initial landscape pattern indices to obtain multiple target landscape pattern indices; Based on the plurality of target landscape pattern indices and the sea ice thickness suitability index, a comprehensive index is obtained, including: Performing index preprocessing on the plurality of landscape pattern indices and the sea ice thickness suitability index by using the first preset formula or the second preset formula to obtain a plurality of transformation indices; Among them, the first preset formula is The second preset formula is Where m represents any one of the landscape pattern index and sea ice thickness suitability index; X nm ′ are all values ​​after exponential preprocessing, X nm is the value of the original index, max X m and min X m Respectively represent the maximum and minimum values ​​of the current index m; Based on multiple transformed indices, a comprehensive index is obtained.

2. The method for determining the suitability of the habitat for harbor seals during their breeding period according to claim 1, characterized in that: Screening the plurality of initial landscape pattern indices to obtain a plurality of target landscape pattern indices includes: By formula The correlation between the multiple initial landscape pattern indexes is calculated to obtain the correlation data between the multiple initial landscape pattern indexes; wherein A is the first initial landscape pattern index, B is the second initial landscape pattern index, r AB is the Pearson correlation coefficient, which is used to measure the linear correlation between two variables A and B. AB ∈[-1,1], l is the total number of samples of variables A and B, i is the current variable, A i is the i-th element of variable A, B i is the i-th element of variable B, is the average value of the elements in A, is the average value of the elements in B; According to the correlation data, the multiple initial landscape pattern indices are screened to obtain multiple target landscape pattern indices.

3. The method for determining the suitability of the habitat for breeding of harbor seals according to claim 1, characterized in that: The sea ice thickness suitability index is obtained through sea ice thickness data, including: Determine the range of sea ice thickness suitable for harbor seal breeding; The sea ice thickness suitability index is obtained by counting the sea ice thickness data on the fishing net using a preset programming language and calculating the ratio of the sea ice area of ​​the sea ice thickness range data corresponding to the sea ice thickness data to the total sea ice area.

4. The method for determining the suitability of the habitat for harbor seals during their breeding period according to claim 1, characterized in that: Based on multiple transformation indices, a comprehensive index is obtained, including: Based on the transformation indexes, determining a correlation coefficient matrix R between the transformation indexes; Based on the correlation coefficient matrix R between the transformed indices, obtaining the eigenvalues ​​of the correlation coefficient matrix R; According to the eigenvalue of the correlation coefficient matrix R, the comprehensive index is obtained.

5. The method for determining the suitability of the habitat for harbor seals during their breeding period according to claim 4, characterized in that: According to the eigenvalues ​​of the correlation coefficient matrix R, the comprehensive index is obtained, including: According to the eigenvalues ​​of the correlation coefficient matrix R, the number of principal components of the transformation index set and the variance contribution rate corresponding to each principal component are determined; Determine eigenvectors based on eigenvalues; According to the number of principal components and eigenvectors, the expressions of each principal component are determined; The comprehensive index is obtained according to the variance contribution rate and principal component expression corresponding to each principal component.

6. A device for determining the suitability of habitat for harbor seals during their breeding period, characterized in that: include: An acquisition module is used to obtain sea ice thickness data for the entire target area; A processing module, used to obtain multiple target landscape pattern indices through sea ice thickness data; Obtaining a sea ice thickness suitability index through sea ice thickness data; obtaining a comprehensive index based on a plurality of target landscape pattern indices and the sea ice thickness suitability index; determining the habitat suitability of harbor seals in the target area during the breeding period through the comprehensive index; Among them, multiple target landscape pattern indices are obtained through sea ice thickness data, including: The sea ice thickness data is classified and processed by a reclassification method to obtain a first sea ice classification result; Based on the target area, the first sea ice classification result is clipped within a preset range to obtain a second sea ice classification result of a preset number of ice periods per day in the target area; Through a preset program, the second sea ice classification result is used to calculate indicators and obtain multiple initial landscape pattern indices; Screening the multiple initial landscape pattern indices to obtain multiple target landscape pattern indices; Based on the plurality of target landscape pattern indices and the sea ice thickness suitability index, a comprehensive index is obtained, including: Performing index preprocessing on the plurality of landscape pattern indices and the sea ice thickness suitability index by using the first preset formula or the second preset formula to obtain a plurality of transformation indices; Among them, the first preset formula is The second preset formula is Where m represents any one of the landscape pattern index and sea ice thickness suitability index; X nm ′ are all values ​​after exponential preprocessing, X nm is the value of the original index, max X m and min X m Respectively represent the maximum and minimum values ​​of the current index m; Based on multiple transformed indices, a comprehensive index is obtained.

7. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 5 is performed.

8. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 5.

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