CN109636171B - Comprehensive diagnosis and risk evaluation method for regional vegetation recovery - Google Patents
Comprehensive diagnosis and risk evaluation method for regional vegetation recovery Download PDFInfo
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Abstract
The invention discloses a comprehensive diagnosis and risk evaluation method for regional vegetation restoration, which is implemented according to the following steps: determining a research area, and collecting annual NDVI remote sensing data of the research area; determining the space-time variation characteristic of vegetation coverage, the trend characteristic of annual vegetation coverage change, the persistence characteristic of annual vegetation coverage change, the mutation characteristic of annual vegetation coverage change and the distribution function characteristic of annual vegetation coverage change; establishing a risk grade corresponding to the vegetation coverage, calculating the risk occurrence probability of each vegetation coverage grade in each vegetation coverage pixel, wherein the risk grade corresponding to the vegetation coverage grade with the highest risk occurrence probability is the vegetation recovery risk grade of the pixel, and the area is a grade risk area. According to the comprehensive diagnosis and risk evaluation method for regional vegetation recovery, disclosed by the invention, the regional vegetation recovery is researched by virtue of less data, and diagnosis and risk evaluation are efficiently carried out, so that a scientific basis is provided for regional ecological construction.
Description
Technical Field
The invention belongs to the technical field of ecological evaluation methods, and relates to a comprehensive diagnosis and risk evaluation method for regional vegetation recovery.
Background
With the construction of modern generations in our country, the technological revolution is continuously carried out, the economic level is continuously improved, the industrialization and the urbanization are rapidly developed, people have more and more tendency to have good life to the ecological environment, the problems of water and soil loss, desertification, drought and the like of the current region are still serious, the problem of resource environment is continuously highlighted, and the ecological environment is urgently managed.
In the energy exchange, water circulation, carbon circulation, biogeochemical circulation and maintenance of the surface ecosystem, the vegetation plays an important role, and the condition of recovering the vegetation in the region has important significance for the change of the ecological environment of the region. Since 1999, with the increase of the Chinese vegetation restoration project, the covering condition of the regional vegetation has changed remarkably. Therefore, the method has very important significance in comprehensive diagnosis and risk assessment of regional vegetation restoration. Currently, methods for comprehensive diagnosis and risk assessment of regional vegetation restoration are relatively few. Therefore, it is very important to provide a comprehensive diagnosis and risk evaluation method for recovering the vegetation in the area.
Disclosure of Invention
The invention aims to provide a comprehensive diagnosis and risk evaluation method for regional vegetation recovery, which improves the efficiency of the comprehensive diagnosis and risk evaluation of the regional vegetation recovery.
The technical scheme adopted by the invention is that the comprehensive diagnosis and risk evaluation method for regional vegetation recovery is implemented according to the following steps:
step 1, determining a research area, and collecting annual NDVI remote sensing data of the research area;
step 2, calculating to obtain annual vegetation coverage time sequence grid data according to the collected annual NDVI data, and counting the maximum value, the minimum value, the average value, the standard deviation and the variation coefficient of the annual vegetation coverage time sequence grid data to determine the space-time variation characteristics of the vegetation coverage;
step 3, obtaining vegetation coverage Mann-Kendall inspection result grid data of different significant levels by a Mann-Kendall trend inspection method according to the annual vegetation coverage time sequence grid data on the basis of the step 2, and analyzing the trend characteristics of the annual vegetation coverage change;
step 4, on the basis of the step 2, calculating annual vegetation coverage Hurst index raster data according to the annual vegetation coverage time sequence raster data, grading the Hurst index raster data, and analyzing to obtain the persistence characteristics of the annual vegetation coverage change;
step 5, on the basis of the step 2, analyzing and obtaining the mutational characteristics of the annual vegetation coverage change according to the annual vegetation coverage time sequence grid data and the annual vegetation coverage obvious mutation annual grid data obtained by a Pettitt mutation point inspection method;
step 6, on the basis of the step 2, analyzing distribution function characteristics of annual vegetation coverage change according to the annual vegetation coverage time sequence grid data and the theoretical optimal distribution grid data of the vegetation coverage obtained by applying a statistical principle;
and 7, establishing risk grades corresponding to the vegetation coverage according to the annual vegetation coverage time sequence grid data and the theoretical optimal distribution grid data and the classification standard of the vegetation coverage from low to high, calculating the risk occurrence probability of each vegetation coverage grade in each vegetation coverage pixel, and if the risk grade corresponding to the vegetation coverage grade with the highest risk occurrence probability is the vegetation recovery risk grade of the pixel, the area is a graded risk area.
The present invention is also characterized in that,
the middle-aged NDVI remote sensing data in the step 1 are obtained by adopting a maximum synthesis method, and the operation steps are as follows:
the method comprises the steps of obtaining month and season NDVI time sequence grid data step by an ArcGIS software Spatial analysis Tools Local tool Cell statics according to a maximum synthesis method, obtaining year NDVI time sequence grid data finally, and converting the year NDVI time sequence grid data into vegetation coverage time sequence grid data by an ArcGIS software Spatial analysis Tools Map Algebra tool scanner.
And 2, calculating the middle-year vegetation coverage data from the NDVI data by adopting a maximum and minimum method, wherein the formula is as follows:
f＝(NDVI-NDVI min )/(NDVI max -NDVI min )
wherein f is the vegetation coverage, NDVI is the vegetation index of the pixel, NDVI max And NDVI Min Respectively, the maximum and minimum values of the NDVI of the study region.
The calculation method of the Mann-Kendall trend test in the step 3 comprises the following steps:
the Mann-Kendall test statistic S was calculated using the following formula:
wherein x is j And x k Sample values corresponding to j and k, n, years>j>k, n refers to the number of samples, where
In the case where the time series are randomly independent, the statistics are defined:
wherein VAR (S) can be calculated by the following formula
Performing trend test by using Z value, wherein positive value of Z represents upward trend, and negative value represents downward trend; the statistic Z follows a standard normal distribution.
The calculation method of the Hurst index in the step 4 comprises the following steps:
the Hurst index can effectively predict the future trend of the time series, define the time series, t =1,2, …, n, calculate the average value of the time series,
the accumulated deviation of the time series is calculated,
the range of the time series is calculated,
a sequence of standard deviations is established which,
and finally, calculating the Hurst index,
wherein H represents the Hurst index, and the value of H is in the range of [0,1 ]; when H =0.5, it indicates that the overlaid time series is a random sequence, unsustainable; when H >0.5, the change of vegetation coverage is basically consistent with the current trend, which indicates that the vegetation sustainability is positive; h <0.5 indicates negative sustainability, and future vegetation coverage changes will be opposite to the current trend.
The calculation method of the Pettitt mutation point test in the step 5 comprises the following steps:
the method divides a time data sequence into a front time section and a rear time section by using time nodes which are possible to generate mutation, and considers that the data of the two time sections are from the same sample; the Pettitt method adopts U in Mann-Whitney t,n Value checking two separate samples x in the same population 1 ,…,x t And x t+1 ,…,x n Statistical quantity U t,n The formula is as follows:
wherein:
the zero hypothesis of the Pettitt test is no change point, when | U t,n X corresponding to when | takes maximum value t The mutation significance level, considered to be a possible mutation point, can be calculated by the following formula:
mean change points were considered to be present in the data when p.ltoreq.0.05.
In the theoretical optimal distribution calculation of step 6, the parameter estimation of four theoretical probability distribution functions is respectively estimated by using a linear moment method, and the calculation formula is as follows:
let the variable X be the vegetation coverage, the vegetation coverage distribution function be F (X) = P (X ≦ X), and there is an inverse function X = G (F), also called quantile function, for the sample X 1 ，...，x n Recording order statistics: x is a radical of a fluorine atom 1:n ≤x 2:n ,....,≤x n:n
Hosking's r-order linear moment method estimator is as follows:
wherein:
in the formula: r is the order of the linear moment, and n is the number of samples.
The optimal theoretical distribution optimization method for the vegetation coverage in the step 6 comprises the following steps:
the distribution function test uses a nonparametric kolmogorov-dominov test and is calculated as follows:
setting Sn (x) as an accumulated probability distribution function of vegetation coverage observed values of random samples, namely an empirical distribution function, and setting the sample amount as n; fo (x) is a specific cumulative probability distribution function, i.e. the theoretical distribution function, defining D = | Sn (x) -Fo (x) |, if for each value of x, sn (x) is close to Fo (x), indicating that the empirical distribution function fits well to the theoretical distribution function, it is reasonable to assume that the sample data is from the population that follows the theoretical distribution;
the K-S test is for the largest deviation in absolute values D = | Sn (x) -Fo (x) |, i.e. the test is done with the following statistics:
D max ＝max|Sn(x)-Fo(x)|；
the risk probability calculation method in the step 7 comprises the following steps:
the risk occurrence probability calculation method is used for calculating according to a probability density formula of continuous random variables, wherein the formula is as follows:
P(x i <x≤x i+1 )＝P(x<x i+1 )-P(x≤x i )
in the formula: m is the grade number of the grading standard from low vegetation coverage to high vegetation coverage; f (x, theta) is the optimal distribution function of the pixel, and theta is a distribution function parameter; x is a radical of a fluorine atom i The maximum value of the interval for coverage classification of the ith level vegetation; p (x is less than or equal to x) i ) Covering degree of vegetation is less than or equal to x i Risk cumulative distribution probability of (a); p (x) i <x≤x i+1 ) The risk occurrence probability of the vegetation coverage occurring in the (i + 1) th interval.
The rapid comprehensive evaluation method for the vegetation recovery of the research area has the advantages that the data acquisition is easy, the calculation is relatively simple, the evaluation efficiency is high, and the comprehensive diagnosis and risk evaluation can be rapidly performed on the vegetation recovery condition of the research area by acquiring the NDVI data of the research area.
Drawings
FIG. 1 is a schematic diagram of the minimum value of perennial vegetation coverage of an uncertain river basin in an example of the comprehensive diagnosis and risk evaluation method for recovering regional vegetation of the present invention;
FIG. 2 is a schematic diagram of the maximum value of the coverage of perennial vegetation in an uncertain river basin in the embodiment of the comprehensive diagnosis and risk evaluation method for recovering the vegetation in the area of the invention;
FIG. 3 is a schematic diagram of mean value of perennial vegetation coverage of the Huang adventitious river basin in an example of the comprehensive diagnosis and risk evaluation method for recovering regional vegetation of the present invention;
FIG. 4 is a differential view of a multi-year vegetation coverage standard of an indeterminate river basin in an example of a method for comprehensive diagnosis and risk assessment of regional vegetation restoration according to the present invention;
FIG. 5 is a schematic diagram illustrating the degree of variation of the coverage of perennial vegetation in an uncertain river basin in the embodiment of the method for comprehensive diagnosis and risk assessment of area vegetation restoration of the present invention;
FIG. 6 is a schematic diagram illustrating a trend of coverage change of perennial vegetation in an uncertain river basin in an example of the comprehensive diagnosis and risk evaluation method for recovering regional vegetation of the present invention;
FIG. 7 is a schematic diagram of the continuity of the coverage change of perennial vegetation in the area of the adventitious river basin in the embodiment of the comprehensive diagnosis and risk assessment method for recovering vegetation in the area of the invention;
FIG. 8 is a schematic diagram of a change mutation point of a perennial vegetation coverage change in an uncertain river basin in an embodiment of the comprehensive diagnosis and risk evaluation method for recovering regional vegetation of the present invention;
FIG. 9 is a schematic diagram of an optimal distribution function of perennial vegetation coverage in an uncertain river basin in an example of the comprehensive diagnosis and risk evaluation method for regional vegetation restoration of the present invention;
FIG. 10 is a schematic view of risk evaluation of vegetation restoration in an indeterminate river basin in an embodiment of the comprehensive diagnosis and risk evaluation method for regional vegetation restoration of the present invention;
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention provides a comprehensive diagnosis and risk evaluation method for regional vegetation recovery based on mathematical statistics and probability theory, which comprises the following specific operation steps:
step 1, determining a research area, and collecting annual NDVI remote sensing data of the research area
And gradually obtaining month and season NDVI time sequence grid data by an ArcGIS software Spatial analysis Tools module Local tool Cell Statistics according to a maximum value synthesis method, finally obtaining year NDVI time sequence grid data, and converting the year NDVI time sequence grid data into vegetation coverage time sequence grid data by an ArcGIS software Spatial analysis Tools Map Algebra tool scanner.
Step 2, calculating to obtain annual vegetation coverage time sequence grid data according to the collected annual NDVI data, and counting the maximum value, the minimum value, the average value, the standard deviation and the variation coefficient of the annual vegetation coverage time sequence grid data to determine the space-time variation characteristics of the vegetation coverage
The annual vegetation coverage data are calculated from NDVI data by adopting a maximum and minimum value method, and the formula is as follows:
f＝(NDVI-NDVI min )/(NDVI max -NDVI min )
wherein f is the vegetation coverage, NDVI is the vegetation index of the pixel, NDVI max And NDVI Min Respectively, the maximum and minimum values of the NDVI of the study region.
Step 3, obtaining the raster data of the inspection results of the vegetation coverage of different significant levels by a Mann-Kendall trend inspection method according to the time sequence raster data of the annual vegetation coverage on the basis of the step 2, and analyzing the trend characteristic of the change of the annual vegetation coverage
The calculation method of the Mann-Kendall trend test comprises the following steps:
the Mann-Kendall test statistic S was calculated using the following formula:
wherein x j And x k Sample values corresponding to j and k, n, respectively, in the year>j>k, n refers to the number of samples, where
In the case where the time series are randomly independent, the statistics are defined:
wherein VAR (S) can be calculated by the following formula
Performing trend test by using Z value, wherein positive value of Z represents upward trend, and negative value represents downward trend; the statistic Z follows a standard normal distribution.
Step 4, on the basis of the step 2, calculating annual vegetation coverage Hurst index raster data according to the annual vegetation coverage time sequence raster data, grading the Hurst index raster data, and analyzing to obtain the persistence characteristics of the annual vegetation coverage change
The calculation method of the Hurst index comprises the following steps:
the Hurst index can effectively predict the future trend of the time series, define the time series, t =1,2, …, n, calculate the average value of the time series,
the accumulated deviation of the time series is calculated,
the range of the time series is calculated,
a sequence of standard deviations is established which,
and finally, calculating the Hurst index,
wherein H represents the Hurst index, and the value of H is in the range of [0,1 ]; when H =0.5, it indicates that the time series covered by the planting is a random sequence, unsustainable; when H >0.5, the change of vegetation coverage is basically consistent with the current trend, which indicates that the vegetation sustainability is positive; h <0.5 indicates negative sustainability, and future vegetation coverage changes will be opposite to the current trend.
Step 5, on the basis of the step 2, according to the annual vegetation coverage time sequence grid data, obtaining the annual vegetation coverage obvious mutation grid data through a Pettitt mutation point inspection method, and analyzing to obtain the mutational characteristics of the annual vegetation coverage change
The calculation method of the Pettitt mutation point test comprises the following steps:
the method divides a time data sequence into a front time section and a rear time section by using time nodes which are possible to generate mutation, and considers that the data of the two time sections are from the same sample; the Pettitt method adopts U in Mann-Whitney t,n Value checking two separate samples x in the same population 1 ,…,x t And x t+1 ,…,x n Statistical quantity U t,n The formula is as follows:
wherein:
the zero hypothesis of the Pettitt test is no change point, when | U t,n X corresponding to when | takes maximum value t The mutation significance level, considered to be a possible mutation point, can be calculated by the following formula:
mean change points were considered to be present in the data when p.ltoreq.0.05.
In the theoretical optimal distribution calculation of step 6, the parameter estimation of four theoretical probability distribution functions is respectively estimated by using a linear moment method, and the calculation formula is as follows:
let the variable X be the vegetation coverage, the vegetation coverage distribution function be F (X) = P (X ≦ X), and there is an inverse function X = G (F), also called quantile function, for the sample X 1 ，...，x n Recording order statistics: x is the number of 1:n ≤x 2:n ,....,≤x n:n ，
Hosking's r-order linear moment method estimator is as follows:
wherein:
in the formula: r is the order of the linear moment, and n is the number of samples.
Step 6, on the basis of the step 2, analyzing distribution function characteristics of annual vegetation coverage change according to the annual vegetation coverage time sequence grid data and the theoretical optimal distribution grid data of the vegetation coverage obtained by applying the statistical principle
The distribution function test uses a nonparametric kolmogorov-dominov test and is calculated as follows:
setting Sn (x) as an accumulated probability distribution function of vegetation coverage observed values of random samples, namely an empirical distribution function, and setting the sample amount as n; fo (x) is a specific cumulative probability distribution function, i.e. the theoretical distribution function, defining D = | Sn (x) -Fo (x) |, if for each value of x, sn (x) is close to Fo (x), indicating that the empirical distribution function fits well to the theoretical distribution function, it is reasonable to assume that the sample data is from the population that follows the theoretical distribution;
the K-S test is for the largest deviation in absolute values D = | Sn (x) -Fo (x) |, i.e. the test is done with the following statistics:
D max ＝max|Sn(x)-Fo(x)|；
step 7, establishing risk levels corresponding to the vegetation coverage according to the annual vegetation coverage time sequence grid data and the theoretical optimal distribution grid data and the classification standard of the vegetation coverage from low to high, calculating the risk occurrence probability of each vegetation coverage level in each vegetation coverage pixel, wherein the risk level corresponding to the vegetation coverage level with the highest risk occurrence probability is the vegetation recovery risk level of the pixel, and the area is a grade risk area
And step 7, the risk probability calculation method comprises the following steps:
the risk occurrence probability calculation method is used for calculating according to a probability density formula of continuous random variables, wherein the formula is as follows:
P(x i <x≤x i+1 )＝P(x<x i+1 )-P(x≤x i )
in the formula: m is the grade number of the grading standard from low vegetation coverage to high vegetation coverage; f (x, theta) is the optimal distribution function of the pixel, and theta is a distribution function parameter; x is a radical of a fluorine atom i The maximum value of the interval of coverage grading of the ith level vegetation; p (x is less than or equal to x) i ) Covering degree of vegetation is less than or equal to x i Risk cumulative distribution probability of (a); p (x) i <x≤x i+1 ) The risk occurrence probability of the vegetation coverage occurring in the (i + 1) th interval.
The invention is described in detail below by taking comprehensive diagnosis and risk evaluation of vegetation restoration in an indeterminate river basin as an example, and is specifically implemented according to the following steps:
step 1, collecting basic data: 1, NDVI data with the resolution of 500m in an unsteady river basin and a Chinese synthesized NDVI data product MODND1T/NDVI have the spatial resolution of 500m multiplied by 500m and the interval of 10 days in the period from 2000 to 2013, wherein the data are provided by a geospatial data cloud site (http:// www.gscloud.cn) of a computer network information center of Chinese academy of sciences;
step 2, according to the collected NDVI data with the Spatial resolution of 500 meters and the interval of 10 days in the period from 2000 to 2013, month and season NDVI time sequence grid data are obtained step by step through an ArcGIS software Spatial analysis Tools module Local Cell Statistics according to a maximum synthesis method, the season NDVI time sequence grid data are synthesized into annual NDVI time sequence grid data by using the maximum synthesis method, the annual NDVI time sequence grid data are converted into vegetation coverage time sequence grid data through an ArcGIS software Spatial analysis Tools module Map Algebra tool scanner calculation, and the annual NDVI time sequence grid data are superposed to form a new grid file through an ArcGIS software Spatial analysis Tools module Local tool combination from 2000 to 2013, exporting a newly generated Raster file attribute table into a txt file, realizing the calculation of a minimum value, a maximum value, a mean value, a standard deviation and a variation coefficient through R language programming, reading the txt file into R language, calculating to obtain a calculation result of 5 statistical characteristics of each pixel and generating a 1.Txt file export, linking the newly generated 1.txt file to the newly generated Raster file through ArcGIS software, generating 5 new Raster files, named as a minimum value, a maximum value, a mean value, a standard deviation and a variation coefficient, by 5 fields of the newly generated Raster file attribute table 1.Txt after the 1.txt file is linked through an ArcGIS software Spatial analysis Tools module Reclass tool Lookup, and grading the variation coefficient (weak variation is 0-0.1, medium variation is 0.1-1, strong variation) 1) such as FIGS. 1,2, 3, 4 and 5;
step 3, according to the calculated annual vegetation coverage time series raster data, overlaying 2000-2013 annual vegetation coverage time series raster data together through an ArcGIS software Spatial analysis Tools module Local tool Commine to generate a new raster file, exporting a newly generated raster file attribute table into a txt file, realizing Man-Kendall inspection calculation through R language programming, reading the txt file into the R language to calculate calculation results of different significant levels of vegetation coverage Man-Kendall inspection and generate a 1.txt file export, linking the newly generated 1.txt file to the newly generated raster file through the ArcGIS software, and naming a new field of a newly generated raster file attribute table 1.txt after the 1.txt file is linked as a new raster file through an ArcGIS software Spatial analysis Tools Lookup, as shown in FIG. 6;
step 4, according to the calculated annual vegetation coverage time series raster data, superimposing 2000-2013 annual vegetation coverage time series raster data together through an ArcGIS software Spatial analysis Tools module Local tool Commine to generate a new raster file, exporting a newly generated raster file attribute table into a txt file, reading the txt file into an R language to calculate the calculation results of Hurst indexes of =0.5 and >0.5 categories and generating a 1.txt file export through the calculation of an R language programming Hurst index, linking the newly generated 1.txt file to the newly generated raster file through the ArcGIS software, and naming a new field of a newly generated raster file attribute table 1.txt after linking the 1.txt file as a new raster file through an ArcGIS software Spatial analysis Tools recycle tool Lookup, as shown in FIG. 7;
step 5, according to the calculated annual vegetation coverage time series raster data, overlaying 2000-2013 annual vegetation coverage time series raster data together through an ArcGIS software Spatial analysis Tools module Local tool combination to generate a new raster file, exporting a newly generated raster file attribute table into a txt file, realizing Pettitt mutation point inspection through R language programming, reading the txt file into R language to calculate a calculation result of significant mutated vegetation coverage and generating a 1.txt file export, linking the newly generated 1.xt file to the newly generated raster file through ArcGIS software, generating a new raster file by using a Lookup of an ArcGIS software Spatial analysis Tools module Loclas tool after the 1.txt file is linked, naming the new raster file as a Pettitt mutation point inspection result, as shown in FIG. 8;
step 6, according to the calculated annual vegetation coverage time sequence raster data, superimposing 2000-2013 annual vegetation coverage time sequence raster data together through an ArcGIS software Spatial analysis Tools module Local tool combination to generate a new raster file, exporting a newly generated raster file attribute table into a txt file, realizing the theoretical optimal distribution function optimization of each pixel through R language programming, reading the txt file into R language to calculate a theoretical optimal distribution function result of each pixel and generating a 1.Txt file export, linking the newly generated 1.txt file to the newly generated raster file through ArcGIS software, and generating a new raster file by using a new field of a raster file attribute table 1.Txt newly generated after the 1.txt file is linked through an ArcGIS software Spatial analysis Tools module Lookup, which is named as an optimal distribution function, as shown in FIG. 9.
Step 7, according to the obtained annual vegetation coverage time sequence raster data, superimposing 2000-2013 annual vegetation coverage time sequence raster data together through an ArcGIS software Spatial analysis Tools module Local tool combination to generate a new raster file, deriving a new raster file attribute table into a v.txt file, sequentially calculating low vegetation coverage (0-0.1), low vegetation coverage (0.1-0.3), medium vegetation coverage (0.3-0.5), high vegetation coverage (0.5-0.7) and Gao Zhi coverage (0.7) according to 5 levels distributed from high to low, wherein the corresponding risk levels are high risk, medium risk, low risk and low risk, on the basis of the step 6, the R language programming is used for judging the occurrence probability and risk level of each level, the v.txt file is read into a calculation language R calculation result time sequence raster data, the calculation result R language is read into a new raster file, the new raster file is generated by reading the new raster file attribute table generated by the ArcgIS software module, and generating a new raster file such as a new raster file generated by a map module 10.
Claims (9)
1. A comprehensive diagnosis and risk evaluation method for regional vegetation recovery is characterized by comprising the following steps:
step 1, determining a research area, and collecting annual NDVI remote sensing data of the research area;
step 2, calculating to obtain annual vegetation coverage time sequence grid data according to the collected annual NDVI data, and counting the maximum value, the minimum value, the average value, the standard deviation and the variation coefficient of the annual vegetation coverage time sequence grid data to determine the space-time variation characteristics of the vegetation coverage;
step 3, obtaining vegetation coverage Mann-Kendall inspection result grid data of different significant levels by a Mann-Kendall trend inspection method according to the annual vegetation coverage time sequence grid data on the basis of the step 2, and analyzing the trend characteristics of the annual vegetation coverage change;
step 4, on the basis of the step 2, calculating annual vegetation coverage Hurst index raster data according to the annual vegetation coverage time sequence raster data, grading the Hurst index raster data, and analyzing to obtain the persistence characteristics of the annual vegetation coverage change;
step 5, analyzing and obtaining the mutational characteristics of the annual vegetation coverage change according to the annual vegetation coverage time sequence grid data and the annual vegetation coverage obvious mutation grid data obtained by a Pettitt mutation point inspection method on the basis of the step 2;
step 6, on the basis of the step 2, analyzing distribution function characteristics of annual vegetation coverage change according to the annual vegetation coverage time sequence grid data and the theoretical optimal distribution grid data of the annual vegetation coverage obtained by applying a statistical principle;
and 7, establishing risk grades corresponding to the vegetation coverage according to the annual vegetation coverage time sequence grid data and the theoretical optimal distribution grid data and the classification standard of the vegetation coverage from low to high, calculating the risk occurrence probability of each vegetation coverage grade in each vegetation coverage pixel, wherein the risk grade corresponding to the vegetation coverage grade with the highest risk occurrence probability is the vegetation recovery risk grade of the pixel, and the area is the equal-grade risk area.
2. The comprehensive diagnosis and risk evaluation method for regional vegetation restoration according to claim 1, wherein the annual NDVI remote sensing data in step 1 are obtained by a maximum synthesis method, and the operation steps are as follows:
the method comprises the steps of obtaining month and season NDVI time sequence grid data step by step through an ArcGIS software Spatial analysis Tools module Local tool Cell statics according to a maximum value synthesis method, obtaining year NDVI time sequence grid data finally, and converting the year NDVI time sequence grid data into vegetation coverage time sequence grid data through an ArcGIS software Spatial analysis Tools module Map Algebra tool scanner.
3. The comprehensive diagnosis and risk evaluation method for regional vegetation restoration according to claim 1, wherein the annual vegetation coverage data in step 2 are calculated from NDVI data by adopting a maximum and minimum method, and the formula is as follows:
f＝(NDVI-NDVI min )/(NDVI max -NDVI min )
wherein f is the vegetation coverage, NDVI is the vegetation index of the pixel, NDVI max And NDVI min Respectively, the maximum and minimum values of the NDVI of the study region.
4. The comprehensive diagnosis and risk evaluation method for regional vegetation restoration according to claim 1, wherein the calculation method of the 3Mann-Kendall trend test is as follows:
the Mann-Kendall test statistic S was calculated using the following formula:
wherein x j And x k Sample values corresponding to j and k, n, respectively, in the year>j>k, n refer to the number of samples, wherein
In the case where the time series are randomly independent, the statistics are defined:
wherein VAR (S) can be calculated by the following formula
Performing trend test by using Z value, wherein positive value of Z represents upward trend, and negative value represents downward trend; the statistic Z follows a standard normal distribution.
5. The comprehensive diagnosis and risk evaluation method for regional vegetation restoration according to claim 1, wherein the calculation method of the Hurst index in step 4 is as follows:
the Hurst index can effectively predict the future trend of the time series, define the time series, t =1,2, …, n, calculate the average value of the time series,
the accumulated deviation of the time series is calculated,
the range of the time series is calculated,
a sequence of standard deviations is established which,
and finally, calculating the Hurst index,
wherein H represents the Hurst index, and the value of H is in the range of [0,1 ]; when H =0.5, it indicates that the overlaid time series is a random sequence, unsustainable; when H >0.5, the change in vegetation coverage is substantially consistent with the current trend, indicating that the vegetation's sustainability is positive; h <0.5 indicates negative sustainability, and future vegetation coverage changes will be opposite to the current trend.
6. The comprehensive diagnosis and risk assessment method for regional vegetation restoration according to claim 5, wherein the calculation method of the Pettitt mutation point test in step 5 is as follows:
the Pettitt method adopts U in Mann-Whitney t,n Value checking two separate samples x in the same population 1 ,…,x t And x t+1 ,…,x n Statistical quantity U t,n The formula of (1) is:
wherein:
the zero hypothesis of the Pettitt test is no change point, when | U t,n X corresponding to when | takes maximum value t The mutation significance level, considered to be a possible mutation point, can be calculated by the following formula:
mean change points were considered to be present in the data when p.ltoreq.0.05.
7. The comprehensive diagnosis and risk evaluation method for regional vegetation recovery according to claim 1, wherein the parameter estimation of the four theoretical probability distribution functions in the theoretical optimal distribution calculation of step 6 is estimated by using a linear moment method, and the calculation formula is as follows:
let the variable X be the vegetation coverage, the vegetation coverage distribution function be F (X) = P (X ≦ X), and there is an inverse function X = G (F), also called quantile function, for the sample X 1 ，...，x n Recording order statistics: x is the number of 1:n ≤x 2:n ,....,≤x n:n ，
Hosking's r-order linear moment method estimator is as follows:
wherein:
in the formula: r is the order of the linear moment, and n is the number of samples.
8. The method for comprehensive diagnosis and risk assessment of regional vegetation restoration according to claim 1, wherein the method for theoretically optimizing the distribution of vegetation coverage in step 6 comprises:
the distribution function test uses a nonparametric kolmogorov-dominov test and is calculated as follows:
the method comprises the following steps of (1) setting Sn (x) as a cumulative probability distribution function of random sample vegetation coverage observed values, namely an empirical distribution function, and setting the sample amount as n; fo (x) is a particular cumulative probability distribution function, i.e. a theoretical distribution function, defining D = | Sn (x) -Fo (x) |, if for each value of x, sn (x) is close to Fo (x), indicating that the empirical distribution function has a high degree of fit to the theoretical distribution function, it is reasonable to consider the sample data to come from a population that obeys the theoretical distribution;
the K-S test is for the largest deviation in absolute values D = | Sn (x) -Fo (x) |, using the following statistics:
D max ＝max|Sn(x)-Fo(x)|。
9. the comprehensive diagnosis and risk evaluation method for regional vegetation restoration according to claim 7, wherein the risk probability calculation method of step 7 is as follows:
the risk occurrence probability calculation method is used for calculating according to a probability density formula of continuous random variables, wherein the formula is as follows:
P(x i ＜x≤x i+1 )＝P(x＜x i+1 )-P(x≤x i )
in the formula: m is the grade number of the grading standard from low vegetation coverage to high vegetation coverage; f (x, theta) is the optimal distribution function of the pixel, and theta is a distribution function parameter; x is the number of i The maximum value of the interval of coverage grading of the ith level vegetation; p (x ≦ x) i ) Covering degree of vegetation is less than or equal to x i Risk cumulative distribution probability of (a); p (x) i <x≤x i+1 ) The risk occurrence probability of the vegetation coverage occurring in the (i + 1) th interval.
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