CN108345897A - A kind of evaluation method of Scenic Bridges index - Google Patents
A kind of evaluation method of Scenic Bridges index Download PDFInfo
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Abstract
The invention discloses a kind of evaluation methods of Scenic Bridges index, belong to landscape ecology technical field.This method includes：Obtain the satellite remote-sensing image data in soil to be measured；According to the satellite remote-sensing image data, interpretation obtains multiple landscape indexes in the soil to be measured；According to multiple landscape indexes in the soil to be measured, calculates and obtain Scenic Bridges index.The Scenic Bridges index obtained using the method for the present invention, landscape fragmentation degree that can accurately in reflecting regional are convenient for the research of Scenic Bridges；This method combines multiple landscape indexes, can be effectively prevented from index skew problem caused by the heterogeneity of region；This method is conducive to the Spatial Distribution Pattern of analyzed area Scenic Bridges；Extracting method of the present invention has the characteristics that general, efficient and easy.
Description
Technical field
The present invention relates to landscape ecology field, more particularly to a kind of evaluation method of Scenic Bridges index.
Background technology
Land use and land cover change is the important component of whole world change, and fragmentation is then landscape pattern drills
The important feature of change has some landscape indexes that can be used for describing the fragmentation in region indirectly, but is the absence of special at present
A kind of Landscape metrics carry out quantitative description degree of fragmentation.
The development of Spatial Data Analysis and software provides technical conditions for research spatial landscape pattern.Referred to using landscape
Exponential model combination space remote sensing calculates, and is carried out in region using effective grid connectivity, morphological feature and shape index etc.
Fragmentation pattern research be common method, but describe fragmentation index still lack unified metering method, it would be highly desirable to open
The method of quantitative study Regional Landscape Fragmentation index can be suitable for by sending out a kind of.
In terms of the analysis of Regional Landscape fragmentation pattern, lack more unified metering method, previous majority is between using
The method for being grounded landscape index carries out research area the quantitative description of globality, especially to the spatial framework of landscape fragmentation
Index is less.Currently, landscape fragmentation pattern research method mainly has：From the side of morphologic angle research fragmentation process
Method；Method based on effective grid assessment and trellis connectivity；Using cuclear density estimation with Moving split-window technique based on road network lattice
The method of office；Forest disintegration dynamic analog based on landscape mosaic degree index；And under Urbanization based on patch
The method of the landscape indexes such as density and shape index.
Invention content
An embodiment of the present invention provides a kind of evaluation method of Scenic Bridges index, with solve to lack in the prior art compared with
The quantitative analysis method of unified landscape fragmentation pattern.In order to the embodiment to disclosure some aspects there are one basic reason
Solution, is shown below simple summary.The summarized section is not extensive overview, nor to determine key/critical component
Or describe the protection domain of these embodiments.Its sole purpose is that some concepts are presented with simple form, in this, as below
Detailed description preamble.
According to an embodiment of the invention, a kind of evaluation method of Scenic Bridges index, including：
Obtain the satellite remote-sensing image data in soil to be measured；
According to satellite remote-sensing image data, interpretation obtains multiple landscape indexes in soil to be measured；
According to multiple landscape indexes in soil to be measured, calculates and obtain Scenic Bridges index.
Multiple landscape indexes and the foundation of Scenic Bridges index are contacted in the method, not only it is possible to prevente effectively from region
Index skew problem caused by heterogeneity, and it is more accurate by the calculated Scenic Bridges index of this method, have logical
With, efficient and easy feature.
In some optional embodiments, according to satellite remote-sensing image data, interpretation obtains the first data matrix；
Dimensionless standard normalized is carried out to first data matrix, obtains the second data matrix Z；
Wherein, first data matrix includes each landscape index obtained by the satellite remote-sensing image data interpretation.
In the present embodiment, the process for obtaining multiple landscape indexes is specifically given.
In some optional embodiments, according to multiple landscape indexes in soil to be measured, calculates acquisition Scenic Bridges and refer to
Several operations, including：
Obtain the correlation matrix R of the second data matrix Z；
According to the correlation matrix R of the second data matrix Z, principal component number m is determined, obtain the data square of each principal component
Battle array Fm；
According to following equation, the Scenic Bridges exponential matrix U in soil to be measured is calculated：
In formula, λkFor the characteristic value of k-th of principal component, FkFor k-th of principal component, λ isK is positive integer, is taken
It is worth ranging from 1~m.
In some optional embodiments, after calculating acquisition Scenic Bridges exponential matrix U, further include：
Space range method dimensionless standard normalized is carried out to the data in U.
In some optional embodiments, the second data matrix Z is specific as follows：
In formula, n is the number of pixel in raster data；I is positive integer, and value range is 1~n；P is that Landscape metrics are total
Number, j are positive integer, and value range is 1~p；xijFor Landscape metrics initial data, be first data matrix data in
I-th of pixel of j Landscape metrics.This gives the formula of the second data matrix Z.
In some optional embodiments, during determining principal component number m, it is big that contribution degree is added up according to principal component
In 90%, m is determined.
In the present embodiment, add up contribution degree to principal component to be defined, it is desirable that its 90% or more, further such that
Last data are more accurate.
In some optional embodiments, the correlation matrix R of the second data matrix Z is specific as follows：
In formula, n is the number of pixel in raster data；P is Landscape metrics sum, and a, b are positive integer, and value range is equal
For 1~p；zlaFor first of sample of a rows of matrix Z, zlbFor first of sample of b rows of matrix Z, l is positive integer, value model
It is 1~n to enclose；For the mean value of a rows of matrix Z,For the mean value of the b rows of matrix Z.
Correlation matrix R is to calculate to obtain by the second data matrix Z, and this gives its specific calculating is public
Formula.
In some optional embodiments, the data matrix F of each principal componentmIt is specific as follows：
Fm’=ejm’×Zij；
In formula, m ' is positive integer, and value range is in 1~p；Fm′For the data of a principal components of m ', ejm’For λjFeature to
Measure ejA components of m ', ZijFor the x after standardizationijData, i are positive integer, and value range is 1~n；P is that Landscape metrics are total
Number, j are positive integer, and value range is 1~p；Wherein, the ejBy the related coefficient square for interpreting the second data matrix Z
Battle array R is found out.
In some optional embodiments, the satellite remote-sensing image data in soil to be measured are obtained, including：
The satellite initial data in the soil to be measured to getting pre-processes；
Establish multistage land use classes index system；
Landscape classification, extraction, and carry out precision test.
Technical solution provided in an embodiment of the present invention can include the following benefits：
(1) the Scenic Bridges index obtained using the method for the present invention, landscape fragmentation that can accurately in reflecting regional
Change degree is convenient for the research of Scenic Bridges；
(2) this method combines multiple landscape indexes, can be effectively prevented from index skew problem caused by the heterogeneity of region；
(3) this method is conducive to the Spatial Distribution Pattern of analyzed area Scenic Bridges；
(4) extracting method of the present invention has the characteristics that general, efficient and easy.
It should be understood that above general description and following detailed description is only exemplary and explanatory, not
It can the limitation present invention.
Description of the drawings
The drawings herein are incorporated into the specification and forms part of this specification, and shows the implementation for meeting the present invention
Example, and be used to explain the principle of the present invention together with specification.
Fig. 1 is Beijing's Scenic Bridges index pattern diagram being calculated in implementation case of the present invention, Fragmentation index number
It is [0,1] to be worth section, and Scenic Bridges of the bigger representative of numerical value under 2km scales are bigger.
Fig. 2 is Beijing's level-one land use pattern distributed data figure in case study on implementation of the present invention.
Fig. 3 is Beijing's two level land use pattern distributed data figure in case study on implementation of the present invention.
Fig. 4 is Beijing's first-level class Landscape metrics datagram in the embodiment of the present invention.
Fig. 5 is Beijing's secondary classification Landscape metrics datagram in the embodiment of the present invention.
Fig. 6 is that Beijing's Scenic Bridges in the embodiment of the present invention calculate the principal component datagram obtained.
Specific implementation mode
The following description and drawings fully show specific embodiments of the present invention, to enable those skilled in the art to
Put into practice them.Embodiment only represents possible variation.Unless explicitly requested, otherwise individual components and functionality is optional, and
And the sequence of operation can change.The part of some embodiments and feature can be included in or replace other embodiments
Part and feature.The range of embodiment of the present invention includes the entire scope of claims and the institute of claims
There is obtainable equivalent.Herein, each embodiment can individually or generally be indicated that this is only with term " invention "
It is merely for convenience, and if in fact disclosing the invention more than one, be not meant to automatically limit the range of the application
For any single invention or inventive concept.Herein, relational terms such as first and second and the like are used only for one
Entity, which either operates to distinguish with another entity or operation, to be existed without requiring or implying between these entities or operation
Any actual relationship or sequence.Moreover, the terms "include", "comprise" or its any other variant be intended to it is non-exclusive
Property include, so that process, method or equipment including a series of elements include not only those elements, but also to include
Each embodiment is described other elements that are not explicitly listed by the way of progressive herein, what each embodiment stressed
All it is difference from other examples, just to refer each other for identical similar portion between each embodiment.For embodiment
For disclosed method, product etc., since it is corresponding with method part disclosed in embodiment, so description is fairly simple,
Reference may be made to the description of the method.
With reference to embodiment, the specific implementation mode of the present invention is described in further detail, but is not limited to this
Invention, only illustrates.
According to an embodiment of the invention, a kind of evaluation method of Scenic Bridges index, including：
Obtain the satellite remote-sensing image data in soil to be measured；
According to satellite remote-sensing image data, interpretation obtains land use data, and calculates multiple scapes using Moving split-window technique
See index；
According to multiple landscape indexes, calculates and obtain Scenic Bridges index.
This method establishes a kind of method of new evaluation Scenic Bridges, in the method by interpreting satellite remote sensing shadow
As data, and then multiple landscape indexes are obtained, then further according to the multiple landscape indexes got, calculates and obtain the plot
Scenic Bridges index.Multiple landscape indexes and the foundation of Scenic Bridges index are contacted in the method, it not only can be effective
Index skew problem caused by the heterogeneity of region is avoided, and more smart by the calculated Scenic Bridges index of this method
Standard has the characteristics that general, efficient and easy.
In some optional embodiments, according to satellite remote-sensing image data, interpretation obtains multiple landscapes in soil to be measured
The operation of index, including：
The satellite remote-sensing image data are interpreted, the first data matrix is obtained；
Dimensionless standard normalized is carried out to the first data matrix, obtains the second data matrix Z；
Wherein, the first data matrix includes the landscape index of each pixel in satellite remote-sensing image data.
In the present embodiment, the process for obtaining multiple landscape indexes is specifically given.
Specifically, can be by using Fragstates3.4/4.2, using Moving split-window technique to satellite remote-sensing image data
It is interpreted, window size 2km*2km, obtains first data matrix in the soil to be measured, the area is had recorded in the matrix
The value of each pixel and multiple landscape indexes corresponding with each pixel in the raster data in domain plot.Then to the first data matrix
It is standardized, obtains the first data matrix Z.
In some optional embodiments, according to multiple landscape indexes in soil to be measured, calculates acquisition Scenic Bridges and refer to
Several operations, including：
Obtain the correlation matrix R of the second data matrix Z；
According to the correlation matrix R of the second data matrix Z, principal component number m is determined, obtain the data square of each principal component
Battle array Fm’；
According to following equation, the Scenic Bridges exponential matrix U in soil to be measured is calculated：
In formula 1, λkFor the characteristic value of k-th of principal component, FkFor k-th of principal component, λ isK is positive integer, is taken
It is worth ranging from 1~m.
The present embodiment shows in particular the calculation formula for calculating Scenic Bridges index of this method foundation.Specifically
, using spatial principal component analysis method, the related coefficient square of the second data matrix Z can be calculated under ArcGIS10.0 platforms
Battle array R.The formula combines multiple Landscape metrics, and the Scenic Bridges index of calculating is more accurate, can accurately reflect soil to be measured
Fragmentation degree.
In some optional embodiments, after calculating acquisition Scenic Bridges exponential matrix U, further include：
Space range method dimensionless standard normalized is carried out to the data in U.
It is standardized by the data to U, each picture in the raster data convenient for showing U in corresponding datagram
The difference of the data of member.
In some optional embodiments, the second data matrix Z is specific as follows：
In formula 2, in formula, n is the number of pixel in raster data；I is positive integer, and value range is 1~n；P refers to for landscape
Mark sum, j are positive integer, and value range is 1~p；xijIt is the data of first data matrix for Landscape metrics initial data
In j-th Landscape metrics i-th of pixel.
This gives the formula of the second data matrix Z.
In some optional embodiments, during determining principal component number m, it is big that contribution degree is added up according to principal component
In 90%, m is determined.
In the present embodiment, add up contribution degree to principal component to be defined, it is desirable that its 90% or more, further such that
Last data are more accurate.
In some optional embodiments, the correlation matrix R of the second data matrix Z is specific as follows：
In formula, n is the number of pixel in raster data；P is Landscape metrics sum, and a, b are positive integer, and value range is
1~p；zlaFor first of sample of a rows of matrix Z, zlbFor first of sample of b rows of matrix Z, l is positive integer, value range
It is 1~n；For the mean value of a rows of matrix Z,For the mean value of the b rows of matrix Z.
Correlation matrix R is to calculate to obtain by the second data matrix Z, and this gives its specific calculating is public
Formula.
In some optional embodiments, the data matrix F of each principal componentm’It is specific as follows：
Fm’=eim′×Zij；(formula 4)
In formula 4, m ' is positive integer, and value range is in 1~p；Fm′For the data of a principal components of m ', ejm’For λjFeature
Vectorial ejA components of m ', ZijFor the x after standardizationijData, i are positive integer, and value range is 1~n；P is Landscape metrics
Sum, j are positive integer, and value range is 1~p；Wherein, the ejBy the related coefficient for interpreting the second data matrix Z
Matrix R is found out.
In some optional embodiments, multiple landscape indexes may particularly include：Landscape degree of polymerization index (AI,
Aggregation Index), Landscape isolation index (DIVISION, Landscape Division Index), landscape patch
Dnesity index (PD, Patch Density) and landscape richness index (PRD, Patch Richness Density).
Landscape index can be set according to demand, it is preferred that above-mentioned four kinds of landscape indexes can be used to integrate to scape
Fragmentation index is seen to be evaluated.
Wherein,In formula, giiFor the similar adjacent patch quantity of corresponding landscape types, maxgii
For the similar adjacent plate number of blocks of maximum of corresponding landscape types；
In formula, aijFor the area of pixel ij, A is the landscape gross area (squares thousand
Rice)；
In PD=N/A formulas, N is patch sum, and A is the landscape gross area (sq-km)；
In PRD=m1/A formulas, m1 is plaque type sum, and A is the landscape gross area (sq-km).
In some optional embodiments, the satellite remote-sensing image data in soil to be measured are obtained, including：
The satellite initial data in the soil to be measured to getting pre-processes；
Establish multistage land use classes index system；
Landscape classification, extraction, and carry out precision test.
Specifically, pretreated operation may include：Registration, atmospheric correction, Band fusion, geometric correction, cutting etc. are
The interpretation of subsequent influence is prepared.
Optionally, according to《Land use status quo investigation technology regulations》Deng in conjunction with the ground category feature in plot, formulating corresponding
Taxonomic hierarchies.
Specifically, the operation of precision test, may be used Kappa coefficient analysis classification of assessment precision；Such as extract 150
Check point carries out precision test, and testing accuracy, 0.8 or more, meets the essence of this method research in 80% or more, Kappa coefficients
Degree requires.
Optionally, moving window can both use rectangular window in the above-described embodiments, can also use circular window.It moves
The size of dynamic window can be set according to provincial characteristics and result of study.
Further, by above-described embodiment, soil to be measured can be obtained in the remotely-sensed data of different periods, i.e. satellite is former
Beginning data, then interpret different periods satellite initial data, using described in above-described embodiment Moving split-window technique and space
Principal Component Analysis calculates the overall target U for obtaining and capable of reflecting landscape fragmentation degree.In above process, synthesis is a variety of
Spacial analytical method uses more time phase sequences on this basis especially in conjunction with the advantages of landscape index method and Moving split-window technique
The Scenic Bridges index that the two-stage landscape classification system of row remote sensing image obtains, higher spatial accuracy disclosure satisfy that space
The variation characteristic of pattern and time series is studied.
In the following, above-mentioned calculating process is described in detail by specific embodiment.
A kind of efficient Fragmentation index evaluation method, includes the following steps：
Obtain the remote sensing image data in region；
According to《Land use status quo investigation technical regulation》Deng formulating the land use two-stage classification index system in region；
Remote Sensing Interpretation, which is carried out, according to two-stage classification index system respectively obtains level-one, two level land use classes data, and
It carries out；
Using the moving window module of Fragstates3.4/4.2, the scape in region is calculated using Moving split-window technique
See index Spatial Distribution Pattern data；
Obtained all landscape indexes are carried out space range method dimensionless standard normalizing by dimensionless standard normalized
Change is handled；
Under ArcGIS10.0 platforms, calculating analysis is carried out using spatial principal component analysis method, obtains the weight of each index
Coefficient；
According to the weight of each index, Scenic Bridges index is obtained using weighted sum method.
Specifically, the evaluation method of the Scenic Bridges index, includes the following steps：
(1) regional remote sensing image capturing and data prediction；
(2) two level land use classes index system is established；
(3) landscape classification, extraction, and carry out precision test；
(4) in Fragstates3.4/4.2, the scape under two-stage land use classes is calculated separately using Moving split-window technique
Index (main landscape index is AI, DIVISION, PD, PRD) is seen, the spatial data of 8 Landscape metrics is obtained；
(5) to the data matrix in (4) to be standardized, normalized matrix Z is obtained, each member in normalized matrix
The data such as formula 2 of element；
(6) under ArcGIS10.0 platforms, using spatial PCA module, (5) is calculated, obtain new principal component
And its weight coefficient, calculating process are as follows：
First, its correlation matrix R is acquired to normalized matrix Z according to formula 3；
Then, the characteristic equation of dematrix R, | R- λ Ip|=0 obtains p characteristic root, according to>=0.90 determines m
Value, and find out λiFeature vector ei, principal component calculation formula is formula 4；
Finally, final result U is obtained according to formula 1.
(7) according to the result of calculation in (6), new overall target is obtained with raster symbol-base under ArcGIS10.0 platforms；
The Fragmentation index calculation formula of this method can be also written as:
(8) index in (6) is subjected to space range method dimensionless standard normalized, numerical value is [0,1], obtains space
Scenic Bridges index.Its calculation formula is：
In formula, UiFor each element in the data of U in (7).
In the following, the above process is described in detail by specific embodiment, due to the data volume involved in the present invention
It is too big, it is not suitable for that specific data result of calculation is presented with the mode of table, therefore calculate by the displaying of the form of datagram
Data result.
Leading software to be applied in the present embodiment is：Fragstates3.4/4.2 and ArcGIS10.0.
Scenic Bridges index calculating process in the whole city of Beijing provided by the embodiment region is as follows：
(1) the satellite remote-sensing image data of summer Landsat-8OLI_TIRS in 2013 are obtained.
(2) remotely-sensed data of (1) is carried out to the pretreatment of data, including：Registration, atmospheric correction, Band fusion, geometry school
Just, cut etc., it prepares for the interpretation of image.
(3) land use pattern two-stage classification index system is established, according to《Land use status quo investigation technology regulations》Deng,
In conjunction with the ground category feature of Beijing, it is as shown in table 1 to formulate taxonomic hierarchies；
1 land use pattern classification indicators of table
(4) remote Sensing Interpretation is carried out according to (2) and (3), using the Classification in Remote Sensing Image technology based on object-oriented, obtains Beijing
Level-one, two level land use data, as shown in Figure 2 and Figure 3.
(5) precision test is extracted 150 check points and is carried out precision inspection using Kappa coefficient analysis classification of assessment precision
It tests, testing accuracy, 0.8 or more, meets the requirement of this method research precision in 80% or more, Kappa coefficients.
(6) by the two-stage land use data in (4), the Moving split-window technique for being utilized respectively Fragstats4.2 calculates it
AI, DIVISION, PD, PRD landscape index (it is 2km × 2km to use rectangular window, window size), obtain 8 Landscape metrics,
Data are as shown in Figure 4, Figure 5.
(7) data matrix of 8 landscape indexes in (6) is standardized, obtains normalized matrix Z,
In formula, n is the number of pixel in raster data；I is positive integer, and value range is 1~n；P is that Landscape metrics are total
Number, j are positive integer, and value range is 1~p；xijFor Landscape metrics initial data, be first data matrix data in
I-th of pixel of j Landscape metrics.
(8) on ArcGIS10.0 platforms, using spatial principal component analysis method and grid computing module, by the number in (7)
It is calculated according to according to following equation, first, its correlation matrix R is acquired to normalized matrix Z,
In formula, n is the number of pixel in raster data；P is Landscape metrics sum, and a, b are positive integer, and value range is
1~p；L is a of matrix Z or first of sample of b rows, zlaFor first of sample of a rows of matrix Z, zlbIt is the of matrix Z
First of sample of b rows, l are positive integer, and value range is 1~n；For the mean value of a rows of matrix Z,For the b of matrix Z
Capable mean value.
Then, the characteristic equation of dematrix R, | R- λ Ip|=0 obtains p characteristic root, according to>=0.90 determines m
Value, and find out λiFeature vector ei, Fragmentation index is calculated according to following equation later：
In formula, xijFor the Landscape metrics initial data in (6), i is the number of pixel in raster data, and j is the scape in (6)
See index number, ejm′For λiFeature vector eiA components of m ', m be principal component number；
Also it can be reduced to：
In formula, λ isFm′For a principal components of m '.
(9) according to the calculating process of (8), four principal components of this degree of fragmentation index are obtained, such as F1, F2, F3 and F4 in Fig. 6
It is shown, according toThe first two principal component, i.e. F1 and F2 are then only chosen to calculate, then the m=2, λ in (8)1
=0.2017, λj=0.02, calculate Fragmentation index U using grid operation.
(10) by index in (9) according toRange method normalized is carried out, it is broken to obtain this landscape
Broken degree index, as shown in Figure 1, wherein UiFor each element in the data of U in (9).
Technical solution provided in an embodiment of the present invention can include the following benefits：
(1) the Scenic Bridges index obtained using the method for the present invention, landscape fragmentation that can accurately in reflecting regional
Change degree is convenient for the research of Scenic Bridges；
(2) this method combines multiple landscape indexes, can be effectively prevented from index skew problem caused by the heterogeneity of region；
(3) this method is conducive to the Spatial Distribution Pattern of analyzed area Scenic Bridges；
(4) extracting method of the present invention has the characteristics that general, efficient and easy.
It should be understood that the invention is not limited in the flow and structure that are described above and are shown in the accompanying drawings,
And various modifications and changes may be made without departing from the scope thereof.The scope of the present invention is only limited by the attached claims
System.
Claims (10)
1. a kind of evaluation method of Scenic Bridges index, which is characterized in that including：
Obtain the satellite remote-sensing image data in soil to be measured；
According to the satellite remote-sensing image data, interpretation obtains multiple landscape indexes in the soil to be measured；
According to multiple landscape indexes in the soil to be measured, calculates and obtain Scenic Bridges index.
2. the method as described in claim 1, which is characterized in that according to the satellite remote-sensing image data, interpret described in obtaining
The operation of multiple landscape indexes in soil to be measured, including：
The satellite remote-sensing image data are interpreted, the first data matrix is obtained；
Dimensionless standard normalized is carried out to first data matrix, obtains the second data matrix Z；
Wherein, first data matrix includes each landscape index obtained by the satellite remote-sensing image data interpretation.
3. method as claimed in claim 2, which is characterized in that according to the multiple landscape index, calculate and obtain landscape fragmentation
The operation of index is spent, including：
Obtain the correlation matrix R of the second data matrix Z；
According to the correlation matrix R of the second data matrix Z, principal component number m is determined, obtain the number of each principal component
According to matrix F；
According to following equation, the Scenic Bridges exponential matrix U in the soil to be measured is calculated：
In formula, λkFor the characteristic value of k-th of principal component, FkFor k-th of principal component, λ isK is positive integer, value range
For 1~m.
4. method as claimed in claim 3, which is characterized in that after calculating the acquisition Scenic Bridges exponential matrix U, also
Including：
Range method dimensionless normalized processing in space is carried out to the data in U.
5. method as claimed in claim 3, which is characterized in that the second data matrix Z is specific as follows：
In formula, n is the number of pixel in raster data；I is positive integer, and value range is 1~n；P is Landscape metrics sum, and j is
Positive integer, value range are 1~p；xijFor Landscape metrics initial data, be first data matrix data in j-th of scape
See i-th of pixel of index.
6. method as claimed in claim 3, which is characterized in that during the determining principal component number m, according to it is main at
Divide accumulative contribution degree to be more than 90%, determines the m.
7. method as claimed in claim 5, which is characterized in that the correlation matrix R of the second data matrix Z is specifically such as
Under：
In formula, n is the number of pixel in raster data；P is Landscape metrics sum, and a, b are positive integer, value range is 1~
p；zlaFor first of sample of a rows of matrix Z, zlbFor first of sample of b rows of matrix Z, l is positive integer, and value range is
1~n；For the mean value of a rows of matrix Z,For the mean value of the b rows of matrix Z.
8. the method for claim 7, which is characterized in that the data matrix F of each principal componentm’It is specific as follows：
Fm’=ejm’×Zij；
In formula, m ' is positive integer, and value range is in 1~p；Fm′For the data of a principal components of m ', ejm’For λjFeature vector ej
A components of m ', ZijFor the x after standardizationijData, i are positive integer, and value range is 1~n；P is Landscape metrics sum, j
For positive integer, value range is 1~p；Wherein, the ejCorrelation matrix R by interpreting the second data matrix Z is asked
Go out.
9. the method as described in any one of claim 1-8, which is characterized in that the multiple landscape index includes：Landscape is poly-
Right Index A I, Landscape isolation index D IVISION, landscape patch dnesity index PD and landscape richness indices P RD.
10. method as claimed in claim 9, which is characterized in that the satellite remote-sensing image data for obtaining soil to be measured, packet
It includes：
The satellite initial data in the soil to be measured got is pre-processed；
Establish multistage land use classes index system；
Landscape classification, extraction, and carry out precision test.
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