CN109636171A - A kind of comprehensive diagnos and risk evaluating method that regional vegetation restores - Google Patents
A kind of comprehensive diagnos and risk evaluating method that regional vegetation restores Download PDFInfo
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Abstract
The invention discloses comprehensive diagnos and risk evaluating method that a kind of regional vegetation restores, are specifically implemented according to the following steps: determining survey region, collection research area year NDVI remotely-sensed data；Determine the Characteristics of spatio-temporal of vegetation coverage, the trend feature of year vegetation coverage variation, the characteristics of SSTA persistence of year vegetation coverage variation, the mutability feature of year vegetation coverage variation, the distribution function feature of year vegetation coverage variation；Establish risk class corresponding with vegetation coverage, calculate the risk probability of happening of each vegetation coverage rank in each vegetation coverage pixel, the corresponding risk class of the maximum vegetation coverage rank of risk probability of happening is the revegetation risk class of the pixel, then the region is the grade risk area.The comprehensive diagnos and risk evaluating method that a kind of regional vegetation of the invention restores efficiently carry out diagnosis and risk assessment to survey region revegetation by less data, provide scientific basis for Regional Eco-construction.
Description
Technical field
The invention belongs to ecological evaluation method technical fields, are related to the comprehensive diagnos and risk of a kind of regional vegetation recovery
Evaluation method.
Background technique
With China modernize construction, the continuous progress of scientific and technological revolution, the continuous improvement of economic level, industrialization and
The fast development of urbanization, the people increasingly yearn for ecological environment good days, at present Regional Erosion and desert
The problems such as changing, is arid is still serious, and resources and environment problems constantly highlight, and ecological environment treatment is extremely urgent.
In the energy exchange of surface ecosystems, water circulation, carbon cycle, biogeochemical cycle and maintenance, vegetation
It plays an important role, the quality of regional vegetation recovery situation, is of great significance to region environment variation.Since
Since 1999, engineering is restored with Vegetation of China and has increased, regional vegetation covering situation has significant change.Therefore, to area
Domain revegetation carries out comprehensive diagnos and risk assessment has a very important significance.Restore comprehensive about regional vegetation at present
The method for closing diagnosis and risk assessment is also fewer.Therefore the comprehensive diagnos and risk assessment that a kind of regional vegetation restores are proposed
Method is highly important.
Summary of the invention
The object of the present invention is to provide comprehensive diagnos and risk evaluating method that a kind of regional vegetation restores, improve area
The comprehensive diagnos of domain revegetation and the efficiency of risk assessment.
The technical scheme adopted by the invention is that comprehensive diagnos and risk evaluating method that a kind of regional vegetation restores, tool
Body follows the steps below to implement:
Step 1, survey region, collection research area year NDVI remotely-sensed data are determined；
Step 2, according to the year NDVI data being collected into, a year vegetation coverage time series raster data is calculated, it is right
Year vegetation coverage time series raster data statistics maximum value, minimum value, average value, standard deviation and the coefficient of variation, with true
Determine the Characteristics of spatio-temporal of vegetation coverage；
Step 3, on the basis of step 2, according to year vegetation coverage time series raster data, pass through Mann-
Kendall trend test method obtains the vegetation coverage Mann-Kendall inspection result raster data of the different levels of signifiance,
Analyze the trend feature of year vegetation coverage variation；
Step 4, on the basis of step 2, a year vegetation is calculated according to year vegetation coverage time series raster data
Coverage Hurst index raster data, to Hurst index raster data and is classified, and analysis obtains year vegetation coverage and becomes
The characteristics of SSTA persistence of change；
Step 5, prominent by Pettitt according to year vegetation coverage time series raster data on the basis of step 2
The vegetation coverage that the height method of inspection obtains significantly is mutated time raster data, and analysis obtains year vegetation coverage variation
Mutability feature；
Step 6, on the basis of step 2, according to year vegetation coverage time series raster data, with statistics original
Manage obtained vegetative coverage topology degree Optimal Distribution raster data, the distribution function feature of analysis year vegetation coverage variation；
Step 7, according to year vegetation coverage time series raster data and theoretical Optimal Distribution raster data, according to plant
The grade scale of coating cover degree from low to high establishes risk class corresponding with vegetation coverage, calculates each vegetation coverage
The risk probability of happening of each vegetation coverage rank in pixel, the maximum vegetation coverage rank pair of risk probability of happening
The risk class answered is the revegetation risk class of the pixel, then the region is the grade risk area.
The features of the present invention also characterized in that
Step 1 middle age NDVI remotely-sensed data is all made of maximum value synthetic method and obtains, and operating procedure is as follows:
By ArcGIS software Spatial Anal yst Tools module Local tool Cell Statistics according to
Maximum value synthetic method gradually obtains the moon, season NDVI time series raster data, finally obtains a year NDVI time series grid number
According to, then pass through ArcGIS software Spatial Analyst Tools module Map Algebra tool Raster Calculator
Year NDVI time series raster data is converted into vegetation coverage time series raster data.
Step 2 middle age vegetation coverage data are all made of maximin method and are calculated by NDVI data, and formula is such as
Under:
F=(NDVI-NDVImin)/(NDVImax-NDVImin)
Wherein, f is vegetation coverage, and NDVI is the vegetation index of pixel, NDVImaxAnd NDVIMinIt is research area respectively
The maximum value and minimum value of NDVI.
The calculation method of the Mann-Kendall trend test of step 3 are as follows:
Mann-Kendall test statistics S is calculated using following formula:
Wherein xjAnd xkIt is the time respectively for the corresponding sample value of j and k, n > j > k, n refer to sample size, wherein
In the case where time series random independent, statistic is defined:
In formula, VAR (S) can be calculated by following formula
Trend analysis is carried out using Z value, the positive value of Z indicates that uptrending, negative value indicate downward trend；Statistic Z clothes
From standardized normal distribution.
The calculation method of the Hurst index of step 4 are as follows:
Hurst index can effectively predicted time sequence future trend, define time series, t=1,2 ..., n,
The average value of time series is calculated,
The cumulative departure of time series is calculated,
The range of time series is calculated,
Standard deviation sequence is established,
Hurst index is finally calculated,
Wherein, H represents Hurst index, and the value of H is in the range of [0,1]；As H=0.5, show vegetative coverage when
Between sequence be random sequence, be unsustainable；As H > 0.5, variation and the current trend basic one of vegetation coverage
It causes, shows that the sustainability of vegetation is positive；H < 0.5 indicates negative sustainability, and the following vegetation variation will be with current trend
On the contrary.
The calculation method that the Pettitt catastrophe point of step 5 is examined are as follows:
Time data sequence is divided into former and later two periods using the timing node that may be mutated by this method, and is recognized
Same sample is all from for the two period datas；Pettitt method is using U in Mann-Whitneyt,nValue examines same totality
In two separated sample x1,…,xtAnd xt+1,…,xn, statistic Ut,nFormula are as follows:
Wherein:
The null hypothesis that Pettitt is examined is no change point, when | Ut,n| corresponding x when being maximizedtBeing considered as can
The catastrophe point of energy, mutation significance can be calculated by following formula:
Think that there are mean value change points in data as p≤0.05.
The parameter Estimation of four kinds of theoretical probability distribution functions uses linear moments method in the theoretical Optimal Distribution calculating of step 6
Estimated respectively, calculation formula is as follows:
If variable X is vegetation coverage, vegetation coverage distribution function is F (x)=P (X≤x), and there are inverse function x
=G (F), G (F) is also known as Kernel smooth, for sample x1..., xn, remember order statistic: x1:n≤x2:n,....,≤xn:n,
The linear moment method estimation of r rank of Hosking is as follows:
Wherein:
In formula: r is linear moment order, and n is number of samples.
The theoretical Optimal Distribution preferred method of the vegetation coverage of step 6 are as follows:
Distribution function inspection is examined using nonparametric Andrei Kolmogorov-Si meter Nuo Fu, and calculation formula is as follows:
If Sn (x) is the cumulative distribution function of random sample vegetation coverage observed value, i.e. empirical distribution function,
Sample size is n；Fo (x) is a specific cumulative distribution function, i.e. theoretic distribution function, defines D=| Sn (x)-Fo
(x) |, if Sn (x) and Fo (x) are close for each x value, then show the fitting of empirical distribution function and theoretic distribution function
Degree is very high, it is reasonable to think sample data from the totality for obeying the theoretical distribution；
K-S examine be absolute value D=| Sn (x)-Fo (x) | in maximum deviation, i.e., examined using following statistic
It tests:
Dmax=max | Sn (x)-Fo (x) |；
The risk probability calculation method of step 7 are as follows:
The calculation method of risk probability of happening is calculated according to the probability density formula of continuous random variable, and formula is such as
Under:
P(xi<x≤xi+1)=P (x < xi+1)-P(x≤xi)
In formula: m is the series of the grade scale of vegetation coverage from low to high；F (x, Θ) is the Optimal Distribution letter of pixel
Number, Θ is distribution function parameter；xiFor the section maximum value of the other vegetation cover classification of i-stage；P (x≤xi) it is vegetation coverage
Less than or equal to xiRisk cumulative distribution probability；P(xi<x≤xi+1) it is that vegetation coverage occurs to send out in the risk in i+1 section
Raw probability.
The invention has the advantages that the rapid integrated evaluation method that a kind of regional vegetation of the invention restores, data obtain
Take be easier, calculate it is relatively easy, evaluation it is high-efficient, by obtain survey region NDVI data can quickly to research area
Revegetation situation in domain carries out comprehensive diagnos and risk assessment.
Detailed description of the invention
Fig. 1 is in the example of comprehensive diagnos and risk evaluating method that a kind of regional vegetation of the invention patent restores without fixed
River valley many years vegetation coverage minimum value schematic diagram；
Fig. 2 is in the example of comprehensive diagnos and risk evaluating method that a kind of regional vegetation of the invention patent restores without fixed
River valley many years vegetation coverage maximum value schematic diagram；
Fig. 3 is yellow nothing in the example of comprehensive diagnos and risk evaluating method that a kind of regional vegetation of the invention patent restores
Determine river valley many years vegetation coverage mean value schematic diagram；
Fig. 4 is in the example of comprehensive diagnos and risk evaluating method that a kind of regional vegetation of the invention patent restores without fixed
River valley many years vegetation coverage standard deviation schematic diagram；
Fig. 5 is in the example of comprehensive diagnos and risk evaluating method that a kind of regional vegetation of the invention patent restores without fixed
River valley many years vegetation coverage degree of variation schematic diagram；
Fig. 6 is in the example of comprehensive diagnos and risk evaluating method that a kind of regional vegetation of the invention patent restores without fixed
River valley many years vegetation coverage variation tendency schematic diagram；
Fig. 7 is in the example of comprehensive diagnos and risk evaluating method that a kind of regional vegetation of the invention patent restores without fixed
River valley many years vegetation coverage changes duration schematic diagram；
Fig. 8 is in the example of comprehensive diagnos and risk evaluating method that a kind of regional vegetation of the invention patent restores without fixed
River valley many years vegetation coverage changes catastrophe point schematic diagram；
Fig. 9 is in the example of comprehensive diagnos and risk evaluating method that a kind of regional vegetation of the invention patent restores without fixed
River valley many years vegetation coverage Optimal Distribution function schematic diagram；
Figure 10 is in the example of comprehensive diagnos and risk evaluating method that a kind of regional vegetation of the invention patent restores without fixed
River valley revegetation risk assessment schematic diagram；
Specific embodiment
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
The present invention is based on mathematical statistics and probability theory methods, propose a kind of comprehensive diagnos and wind that regional vegetation restores
Dangerous evaluation method, specific steps are as follows:
Step 1, survey region, collection research area year NDVI remotely-sensed data are determined
By ArcGIS software Spatial Anal yst Tools module Local tool Cell Statistics according to
Maximum value synthetic method gradually obtains the moon, season NDVI time series raster data, finally obtains a year NDVI time series grid number
According to, then pass through ArcGIS software Spatial Analyst Tools module Map Algebra tool Raster Calculator
Year NDVI time series raster data is converted into vegetation coverage time series raster data.
Step 2, according to the year NDVI data being collected into, a year vegetation coverage time series raster data is calculated, it is right
Year vegetation coverage time series raster data statistics maximum value, minimum value, average value, standard deviation and the coefficient of variation, with true
Determine the Characteristics of spatio-temporal of vegetation coverage
Year vegetation coverage data are all made of maximin method and are calculated by NDVI data, and formula is as follows:
F=(NDVI-NDVImin)/(NDVImax-NDVImin)
Wherein, f is vegetation coverage, and NDVI is the vegetation index of pixel, NDVImaxAnd NDVIMinIt is research area respectively
The maximum value and minimum value of NDVI.
Step 3, on the basis of step 2, according to year vegetation coverage time series raster data, pass through Mann-
Kendall trend test method obtains the vegetation coverage Mann-Kendall inspection result raster data of the different levels of signifiance,
Analyze the trend feature of year vegetation coverage variation
The calculation method of Mann-Kendall trend test are as follows:
Mann-Kendall test statistics S is calculated using following formula:
Wherein xjAnd xkIt is the time respectively for the corresponding sample value of j and k, n > j > k, n refer to sample size, wherein
In the case where time series random independent, statistic is defined:
In formula, VAR (S) can be calculated by following formula
Trend analysis is carried out using Z value, the positive value of Z indicates that uptrending, negative value indicate downward trend；Statistic Z clothes
From standardized normal distribution.
Step 4, on the basis of step 2, a year vegetation is calculated according to year vegetation coverage time series raster data
Coverage Hurst index raster data, to Hurst index raster data and is classified, and analysis obtains year vegetation coverage and becomes
The characteristics of SSTA persistence of change
The calculation method of Hurst index are as follows:
Hurst index can effectively predicted time sequence future trend, define time series, t=1,2 ..., n,
The average value of time series is calculated,
The cumulative departure of time series is calculated,
The range of time series is calculated,
Standard deviation sequence is established,
Hurst index is finally calculated,
Wherein, H represents Hurst index, and the value of H is in the range of [0,1]；As H=0.5, show vegetative coverage when
Between sequence be random sequence, be unsustainable；As H > 0.5, variation and the current trend basic one of vegetation coverage
It causes, shows that the sustainability of vegetation is positive；H < 0.5 indicates negative sustainability, and the following vegetation variation will be with current trend
On the contrary.
Step 5, prominent by Pettitt according to year vegetation coverage time series raster data on the basis of step 2
The vegetation coverage that the height method of inspection obtains significantly is mutated time raster data, and analysis obtains year vegetation coverage variation
Mutability feature
The calculation method that Pettitt catastrophe point is examined are as follows:
Time data sequence is divided into former and later two periods using the timing node that may be mutated by this method, and is recognized
Same sample is all from for the two period datas；Pettitt method is using U in Mann-Whitneyt,nValue examines same totality
In two separated sample x1,…,xtAnd xt+1,…,xn, statistic Ut,nFormula are as follows:
Wherein:
The null hypothesis that Pettitt is examined is no change point, when | Ut,n| corresponding x when being maximizedtBeing considered as can
The catastrophe point of energy, mutation significance can be calculated by following formula:
Think that there are mean value change points in data as p≤0.05.
The parameter Estimation of four kinds of theoretical probability distribution functions uses linear moments method in the theoretical Optimal Distribution calculating of step 6
Estimated respectively, calculation formula is as follows:
If variable X is vegetation coverage, vegetation coverage distribution function is F (x)=P (X≤x), and there are inverse function x
=G (F), G (F) is also known as Kernel smooth, for sample x1..., xn, remember order statistic: x1:n≤x2:n,....,≤xn:n,
The linear moment method estimation of r rank of Hosking is as follows:
Wherein:
In formula: r is linear moment order, and n is number of samples.
Step 6, on the basis of step 2, according to year vegetation coverage time series raster data, with statistics original
Manage obtained vegetative coverage topology degree Optimal Distribution raster data, the distribution function feature of analysis year vegetation coverage variation
Distribution function inspection is examined using nonparametric Andrei Kolmogorov-Si meter Nuo Fu, and calculation formula is as follows:
If Sn (x) is the cumulative distribution function of random sample vegetation coverage observed value, i.e. empirical distribution function,
Sample size is n；Fo (x) is a specific cumulative distribution function, i.e. theoretic distribution function, defines D=| Sn (x)-Fo
(x) |, if Sn (x) and Fo (x) are close for each x value, then show the fitting of empirical distribution function and theoretic distribution function
Degree is very high, it is reasonable to think sample data from the totality for obeying the theoretical distribution；
K-S examine be absolute value D=| Sn (x)-Fo (x) | in maximum deviation, i.e., examined using following statistic
It tests:
Dmax=max | Sn (x)-Fo (x) |；
Step 7, according to year vegetation coverage time series raster data and theoretical Optimal Distribution raster data, according to plant
The grade scale of coating cover degree from low to high establishes risk class corresponding with vegetation coverage, calculates each vegetation coverage
The risk probability of happening of each vegetation coverage rank in pixel, the maximum vegetation coverage rank pair of risk probability of happening
The risk class answered is the revegetation risk class of the pixel, then the region is the grade risk area
Step 7 risk probability calculation method are as follows:
The calculation method of risk probability of happening is calculated according to the probability density formula of continuous random variable, and formula is such as
Under:
P(xi<x≤xi+1)=P (x < xi+1)-P(x≤xi)
In formula: m is the series of the grade scale of vegetation coverage from low to high；F (x, Θ) is the Optimal Distribution letter of pixel
Number, Θ is distribution function parameter；xiFor the section maximum value of the other vegetation cover classification of i-stage；P (x≤xi) it is vegetation coverage
Less than or equal to xiRisk cumulative distribution probability；P(xi<x≤xi+1) it is that vegetation coverage occurs to send out in the risk in i+1 section
Raw probability.
The present invention is discussed in detail using the comprehensive diagnos of Wudinghe River Catchment revegetation and risk assessment as example below, has
Body follows the steps below to implement:
Step 1, basic data: 1 > Wudinghe River Catchment 500m resolution, N DVI data, the NDVI of China's synthesis are collected first
Data product MODND1T/NDVI, during 2000 to 2013, spatial resolution is 500 meters × 500 meters, is divided into 10
Its above data by Computer Network Information Center, Chinese Academy of Sciences's geographical spatial data cloud website (http: //
Www.gscloud.cn it) provides；
Step 2, according to during 2000 to 2013 be collected into, spatial resolution is 500 meters, is divided into 10 days
NDVI data, by ArcGIS software Spatial Analyst Tools module Local tool Cell Statistics according to
Maximum value synthetic method gradually obtains the moon and season NDVI time series raster data, reuses maximum value synthetic method for season
NDVI time series raster data synthesizes a year NDVI time series raster data, then passes through ArcGIS software Spatial
Analyst Tools module Map Algebra tool Raster Calculator converts year NDVI time series raster data
For vegetation coverage time series raster data, pass through ArcGIS software Spatial Analyst Tools module Local work
2000-2013 vegetation coverage time series raster data is superimposed together by tool Combine generates new raster file,
Newly-generated raster file attribute list is exported into txt file, minimum value, maximum value, mean value, mark are realized by R Programming with Pascal Language
The calculated result of each 5 statistical natures of pixel is calculated in txt file reading R language by quasi- poor, the coefficient of variation the calculating
And the export of 1.txt file is generated, newly-generated 1.txt file is linked to by newly-generated raster file by ArcGIS software,
It will be linked by ArcGIS software Spatial Analyst Tools module Reclass tool Lookup new after 1.txt file
5 fields of the raster file attribute list 1.txt of generation generate 5 new raster files, be named as minimum value, maximum value,
Mean value, standard deviation, the coefficient of variation are classified (weak variation 0~0.1, medium variation 0.1~1, Qiang Bianyi) to the coefficient of variation
1), such as Fig. 1,2,3,4,5；
Step 3, according to the year vegetation coverage time series raster data being calculated, pass through ArcGIS software
Spatial Analyst Tools module Local tool Combine is by 2000-2013 vegetation coverage time series grid
Stacked data is added together to generate new raster file, and newly-generated raster file attribute list is exported to txt file, passes through R language
Mann-Kendall checking computation is realized in speech programming, and txt file reading R language is calculated to the vegetation of the different levels of signifiance
The calculated result of coverage Mann-Kendall inspection simultaneously generates the export of 1.txt file, will be newly-generated by ArcGIS software
1.txt file is linked to newly-generated raster file, passes through ArcGIS software Spatial Analyst Tools module
Reclass tool Lookup generates the newer field of raster file attribute list 1.txt newly-generated after link 1.txt file new
Raster file, be named as MK inspection, such as Fig. 6；
Step 4, according to the year vegetation coverage time series raster data being calculated, pass through ArcGIS software
Spatial Analyst Tools module Local tool Combine is by 2000-2013 vegetation coverage time series grid
Stacked data is added together to generate new raster file, and newly-generated raster file attribute list is exported to txt file, passes through R language
The Hurst of≤0.5 He > 0.5 two kind classification is calculated in txt file reading R language by the calculating of speech programming Hurst index
The calculated result of index simultaneously generates the export of 1.txt file, is linked to newly-generated 1.txt file newly by ArcGIS software
The raster file of generation, by ArcGIS software Spatial Analyst Tools module Reclass tool Lookup by chain
The newer field for meeting newly-generated raster file attribute list 1.txt after 1.txt file generates new raster file, is named as
Hurst index, such as Fig. 7；
Step 5, according to the year vegetation coverage time series raster data being calculated, pass through ArcGIS software
Spatial Analyst Tools module Local tool Combine is by 2000-2013 vegetation coverage time series grid
Stacked data is added together to generate new raster file, and newly-generated raster file attribute list is exported to txt file, passes through R language
Speech programming realizes that Pettitt catastrophe point is examined, and the vegetation coverage being significantly mutated is calculated in txt file reading R language
Calculated result simultaneously generates the export of 1.txt file, is linked to newly-generated 1.txt file by ArcGIS software newly-generated
Raster file will link 1.txt by ArcGIS software Spatial Analyst Tools module Reclass tool Lookup
A field of newly-generated raster file attribute list 1.txt generates a new raster file after file, is named as
Pettitt catastrophe point inspection result, such as Fig. 8；
Step 6, according to the year vegetation coverage time series raster data being calculated, pass through ArcGIS software
Spatial Analyst Tools module Local tool Combine is by 2000-2013 vegetation coverage time series grid
Stacked data is added together to generate new raster file, and newly-generated raster file attribute list is exported to txt file, passes through R language
Speech programming realizes that the theoretical Optimal Distribution function of each pixel is preferred, and each pixel is calculated in txt file reading R language
Theoretical Optimal Distribution function result and generate the export of 1.txt file, by ArcGIS software by newly-generated 1.txt file
It is linked to newly-generated raster file, passes through ArcGIS software Spatial Analyst Tools module Reclass tool
The newer field of raster file attribute list 1.txt newly-generated after link 1.txt file is generated new raster file by Lookup,
It is named as Optimal Distribution function, such as Fig. 9.
Step 7, according to the year vegetation coverage time series raster data being calculated, pass through ArcGIS software
Spatial Analyst Tools module Local tool Combine is by 2000-2013 vegetation coverage time series grid
Stacked data is added together to generate new raster file, and newly-generated raster file attribute list is exported to v.txt file, vegetation
Coverage is followed successively by low vegetation coverage (0~0.1), lower vegetation coverage (0.1 according to 5 grades are distributed from high to low
~0.3), moderate vegetation coverage (0.3~0.5), higher vegetation coverage (0.5~0.7), high vegetation cover general degree (> 0.7),
Corresponding risk class be high risk, high risk, moderate risk, compared with low-risk, low-risk, in the basis of step 6
On, the judgement of each rank risk probability of happening of each pixel and risk class is realized by R Programming with Pascal Language, by v.txt text
Part reads in R language and vegetation coverage risk class result is calculated and generates the export of 1.txt file, passes through ArcGIS software
Newly-generated 1.txt file is linked to newly-generated vegetation coverage raster file, passes through ArcGIS software Spatial
Analyst Tools module Reclass tool Lookup will link newly-generated raster file attribute list after 1.txt file
The newer field of 1.txt generates a new raster file, is named as risk assessment, such as Figure 10.
Claims (9)
1. comprehensive diagnos and risk evaluating method that a kind of regional vegetation restores, which is characterized in that specifically real according to the following steps
It applies:
Step 1, survey region, collection research area year NDVI remotely-sensed data are determined；
Step 2, according to the year NDVI data being collected into, a year vegetation coverage time series raster data is calculated, year is planted
Coating cover degree time series raster data statistics maximum value, minimum value, average value, standard deviation and the coefficient of variation, are planted with determining
The Characteristics of spatio-temporal of coating cover degree；
Step 3, on the basis of step 2, according to year vegetation coverage time series raster data, become by Mann-Kendall
The gesture method of inspection obtains the vegetation coverage Mann-Kendall inspection result raster data of the different levels of signifiance, analyzes year vegetation
The trend feature of coverage variation；
Step 4, on the basis of step 2, a year vegetative coverage is calculated according to year vegetation coverage time series raster data
Hurst index raster data is spent, to Hurst index raster data and is classified, analysis obtains year vegetation coverage variation
Characteristics of SSTA persistence；
Step 5, on the basis of step 2, according to year vegetation coverage time series raster data, pass through Pettitt catastrophe point
The vegetation coverage that the method for inspection obtains significantly is mutated time raster data, and analysis obtains a year mutability for vegetation coverage variation
Feature；
Step 6, it on the basis of step 2, according to year vegetation coverage time series raster data, is obtained with Principle of Statistics
Vegetative coverage topology degree Optimal Distribution raster data, analysis year vegetation coverage variation distribution function feature；
Step 7, according to year vegetation coverage time series raster data and theoretical Optimal Distribution raster data, according to vegetative coverage
The grade scale of degree from low to high, establishes risk class corresponding with vegetation coverage, calculates in each vegetation coverage pixel
The risk probability of happening of each vegetation coverage rank, the corresponding risk of the maximum vegetation coverage rank of risk probability of happening
Grade is the revegetation risk class of the pixel, then the region is the grade risk area.
2. comprehensive diagnos and risk evaluating method that a kind of regional vegetation according to claim 1 restores, which is characterized in that
Step 1 middle age NDVI remotely-sensed data is all made of maximum value synthetic method and obtains, and operating procedure is as follows:
By ArcGIS software Spatial Anal yst Tools module Local tool Cell Statistics according to maximum
Value synthetic method gradually obtains the moon, season NDVI time series raster data, finally obtains a year NDVI time series raster data, then
By ArcGIS software Spatial Analyst Tools module Map Algebra tool Raster Calculator by year
NDVI time series raster data is converted to vegetation coverage time series raster data.
3. comprehensive diagnos and risk evaluating method that a kind of regional vegetation according to claim 1 restores, which is characterized in that
Step 2 middle age vegetation coverage data are all made of maximin method and are calculated by NDVI data, and formula is as follows:
F=(NDVI-NDVImin)/(NDVImax-NDVImin)
Wherein, f is vegetation coverage, and NDVI is the vegetation index of pixel, NDVImaxAnd NDVIMinIt is research area NDVI respectively
Maximum value and minimum value.
4. comprehensive diagnos and risk evaluating method that a kind of regional vegetation according to claim 1 restores, which is characterized in that
The calculation method of step 3Mann-Kendall trend test are as follows:
Mann-Kendall test statistics S is calculated using following formula:
Wherein xjAnd xkIt is the time respectively for the corresponding sample value of j and k, n > j > k, n refer to sample size, wherein
In the case where time series random independent, statistic is defined:
In formula, VAR (S) can be calculated by following formula
Trend analysis is carried out using Z value, the positive value of Z indicates that uptrending, negative value indicate downward trend；Statistic Z obeys mark
Quasi normal distribution.
5. comprehensive diagnos and risk evaluating method that a kind of regional vegetation according to claim 1 restores, which is characterized in that
The calculation method of step 4Hurst index are as follows:
Hurst index can effectively predicted time sequence future trend, define time series, t=1,2 ..., n, when calculating
Between sequence average value,
The cumulative departure of time series is calculated,
The range of time series is calculated,
Standard deviation sequence is established,
Hurst index is finally calculated,
Wherein, H represents Hurst index, and the value of H is in the range of [0,1]；As H=0.5, show the time sequence of vegetative coverage
Column are random sequences, are unsustainable；As H > 0.5, the variation of vegetation coverage is almost the same with current trend, shows
The sustainability of vegetation is positive；H < 0.5 indicates negative sustainability, and the following vegetation variation will be opposite with current trend.
6. comprehensive diagnos and risk evaluating method that a kind of regional vegetation according to claim 5 restores, which is characterized in that
The calculation method that the Pettitt catastrophe point of step 5 is examined are as follows:
Pettitt method is using U in Mann-Whitneyt,nValue examines two separated sample x in same totality1,…,xtWith
xt+1,…,xn, statistic Ut,nFormula are as follows:
Wherein:
The null hypothesis that Pettitt is examined is no change point, when | Ut,n| corresponding x when being maximizedtIt is considered possible prominent
Height, mutation significance can be calculated by following formula:
Think that there are mean value change points in data as p≤0.05.
7. comprehensive diagnos and risk evaluating method that a kind of regional vegetation according to claim 1 restores, which is characterized in that
The parameter Estimation of four kinds of theoretical probability distribution functions is estimated respectively using linear moments method in step 6 theory Optimal Distribution calculating
Meter, calculation formula are as follows:
If variable X is vegetation coverage, vegetation coverage distribution function is F (x)=P (X≤x), and there are inverse function x=G
(F), G (F) is also known as Kernel smooth, for sample x1..., xn, remember order statistic: x1:n≤x2:n,....,≤xn:n,
The linear moment method estimation of r rank of Hosking is as follows:
Wherein:
In formula: r is linear moment order, and n is number of samples.
8. comprehensive diagnos and risk evaluating method that a kind of regional vegetation according to claim 1 restores, which is characterized in that
The theoretical Optimal Distribution preferred method of step 6 vegetation coverage are as follows:
Distribution function inspection is examined using nonparametric Andrei Kolmogorov-Si meter Nuo Fu, and calculation formula is as follows:
If Sn (x) is the cumulative distribution function of random sample vegetation coverage observed value, i.e. empirical distribution function, sample size
For n；Fo (x) is a specific cumulative distribution function, i.e. theoretic distribution function, defines D=| Sn (x)-Fo (x) |, such as
For fruit for each x value, Sn (x) and Fo (x) are close, then show the fitting degree of empirical distribution function and theoretic distribution function very
It is high, it is reasonable to think sample data from the totality for obeying the theoretical distribution；
K-S examine be absolute value D=| Sn (x)-Fo (x) | in maximum deviation, i.e., examined using following statistic:
Dmax=max | Sn (x)-Fo (x) |.
9. comprehensive diagnos and risk evaluating method that a kind of regional vegetation according to claim 7 restores, which is characterized in that
Step 7 risk probability calculation method are as follows:
The calculation method of risk probability of happening is calculated according to the probability density formula of continuous random variable, and formula is as follows:
P(xi<x≤xi+1)=P (x < xi+1)-P(x≤xi)
In formula: m is the series of the grade scale of vegetation coverage from low to high；F (x, Θ) is the Optimal Distribution function of pixel, Θ
For distribution function parameter；xiFor the section maximum value of the other vegetation cover classification of i-stage；P(x≤xi) be less than for vegetation coverage etc.
In xiRisk cumulative distribution probability；P(xi<x≤xi+1) it is risk probability of happening of the vegetation coverage generation in i+1 section.
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