CN114398760A - Method for identifying inconsistency of regional vegetation coverage and precipitation relation - Google Patents

Method for identifying inconsistency of regional vegetation coverage and precipitation relation Download PDF

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CN114398760A
CN114398760A CN202111561902.9A CN202111561902A CN114398760A CN 114398760 A CN114398760 A CN 114398760A CN 202111561902 A CN202111561902 A CN 202111561902A CN 114398760 A CN114398760 A CN 114398760A
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vegetation coverage
precipitation
relation
aic
model
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贾路
徐国策
任宗萍
李占斌
李鹏
鲁克新
程圣东
于坤霞
成玉婷
王飞超
朱田甜
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Xian University of Technology
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Abstract

The invention discloses a method for identifying the inconsistency of regional vegetation coverage and precipitation relation, which specifically comprises the following steps: firstly, determining a research area, and collecting NDVI and precipitation data of the research area; carrying out precipitation interpolation, and calculating to obtain vegetation coverage data; constructing a plurality of possible scenes of consistency and non-consistency of regional vegetation coverage and precipitation relation based on a GALSS model, selecting a standby probability distribution function, and respectively calculating AIC values; adopting an AIC minimum criterion, preferably an optimal probability distribution function; optimizing a relation model between vegetation coverage and precipitation by adopting an AIC minimum criterion; identifying whether the relation between vegetation coverage and precipitation is inconsistent or not; the spatial distribution of the type of the non-uniform relation between the vegetation coverage of the area and the precipitation is identified, and the method can accurately and effectively identify the non-uniform spatial distribution between the vegetation coverage of the area and the precipitation.

Description

Method for identifying inconsistency of regional vegetation coverage and precipitation relation
Technical Field
The invention belongs to the technical field of a regional vegetation coverage change and precipitation relation diagnosis method, and relates to a regional vegetation coverage and precipitation relation non-consistency identification method.
Background
At present, regional soil and water loss is just serious, biodiversity suffers damage, climatic change aggravates, extreme hydrological events are frequent, and ecological environment management suffers serious challenges. With the development of social economy in China, people tend to better life, and ecological environment protection is reluctant. Under the combined action of climate change and human activities, the regional ecological environment and natural conditions change remarkably, which brings great influence on the life and production of local people, and environmental protection, regional ecological health and high quality become a great national strategy.
In 1999, the policy of returning to forest from cultivation is implemented on a large scale in loess plateau areas, the vegetation of the areas is remarkably increased, and the erosion of the soil in the drainage basin is effectively reduced. Vegetation plays an important role in energy transfer, maintenance and optimization of ecosystem services between land-gas systems, and is an important "indicator" of global ecological environment changes. With the high concern of academic circles on the problem of influence of global climate change and human activity on regional development, the research on the relationship between the terrestrial ecosystem and climate change has long been one of the hot scientific problems in international research. Many research results indicate that vegetation change has obvious response to climate factor change, and the spatial heterogeneity of climate conditions and background environment on a regional scale has a determining effect on vegetation change. Although past research has achieved abundant results, previous researches on climate factor and vegetation change mainly focus on the influence of climate change trend or linear change on vegetation, and theories and models about the nonlinear change of binary relations between the climate factor and the vegetation are lacked. There is a crucial fact that since most of the past researches on vegetation variation are based on univariate statistical means for analyzing the vegetation variation, such as coefficient of variation and Mann-Kendall trend test, and simple researches on multiple base linear methods of response relationship of vegetation and climate factors, such as linear correlation test, linear regression model, and the like, the large difference of variation among variables is easily caused. The surface water and the ground water, the vegetation, the atmosphere, the soil and other environmental elements in nature interact, the nonlinear relation is extremely complex, and the single variable analysis method is favorable for simplifying the model and reducing the workload. However, the analysis method of the univariate only focuses on the change of the single variable (such as precipitation or vegetation), which is not realistic and the response relationship between the climate change and the vegetation change is difficult to be completely revealed.
Therefore, it is necessary to study the non-uniformity change characteristics of the relationship between vegetation coverage and precipitation based on the time-varying visual angle, and it is very important to provide a method for identifying the non-uniformity of the relationship between vegetation coverage and precipitation based on the GALSS function.
Disclosure of Invention
The invention aims to provide a method for identifying the inconsistency of the relation between the vegetation coverage and the rainfall in a region, which can efficiently and accurately identify the inconsistency of the relation between the vegetation coverage and the rainfall in the region.
The technical scheme adopted by the invention is that the method for identifying the inconsistency of the regional vegetation coverage and precipitation relation specifically comprises the following steps:
step 1, determining a research area, and collecting annual NDVI grid data of the research area and annual water lowering data of a meteorological station;
step 2, carrying out interpolation according to the collected annual precipitation data of the meteorological stations in the research area and the area surrounding the area to obtain annual precipitation raster data with the same spatial resolution as the NDVI data, and meanwhile calculating according to the NDVI raster data to obtain vegetation coverage raster data;
step 3, constructing a plurality of possible scenes of consistency and non-consistency of the regional vegetation coverage and the rainfall relation based on a GALSS model according to the annual vegetation coverage grid data and the annual rainfall grid data obtained in the step 2, selecting a plurality of standby probability distribution functions as distribution functions of explained variables in the model under each scene, and respectively calculating the AIC value of the vegetation coverage and the rainfall relation model under each standby probability distribution function under each scene;
step 4, selecting an optimal probability distribution function from several standby probability distribution functions under the condition of consistency and inconsistency of the relation between coverage and rainfall of each planting by adopting the minimum AIC criterion according to the calculation result of the step 3;
step 5, on the basis of the calculation in the step 4, selecting an optimal probability distribution function from a plurality of optimal probability distribution functions of the scenes with consistent and inconsistent relation between the vegetation coverage and the rainfall by adopting an AIC minimum criterion as a relation model between the vegetation coverage and the rainfall;
step 6, comparing the AIC value of the relation model between the vegetation coverage and the precipitation selected in the step 5 with the AIC value of the optimal probability distribution function model under the condition that the vegetation coverage and the precipitation are consistent, and identifying whether the relation between the vegetation coverage and the precipitation is inconsistent or not;
and 7, judging the spatial distribution of the non-uniform relation type between the vegetation coverage of the area and the precipitation according to the calculation result of the step 6.
The invention is also characterized in that:
the specific process of the step 2 is as follows:
the method comprises the following steps of utilizing ArcGIS software to carry out Kriging interpolation to obtain annual precipitation Raster data which are the same as the Spatial resolution of NDVI data, meanwhile, obtaining vegetation coverage Raster data through calculation of a Map Algebra tool router according to the NDVI Raster data through an ArcGIS software Spatial analysis Tools module, wherein the vegetation coverage Raster data are obtained through calculation of the NDVI data by adopting a maximum and minimum value method, and the formula is as follows:
f＝(NDVI-NDVImin)/(NDVImax-NDVImin) (1)；
wherein f is the vegetation coverage, NDVI is the vegetation index of the pixel, NDVImaxAnd NDVIminRespectively, the maximum and minimum values of the NDVI of the study region.
The specific process of the step 3 is as follows:
the GALSS model sets the distribution function of the interpreted variables to normal distribution, exponential distribution, Weber distribution, and the mean and variance of the above distribution function are set to four scenarios: (a) both the mean and the variance are unchanged; (b) the mean value changes with the change of the explanatory variable, and the variance does not change; (c) the mean is unchanged, and the variance changes with the change of the explanatory variable; (d) the mean and variance both vary with the interpretive variable, and the parameters and interpretive variables are expressed as follows:
θi＝f(x) (2)；
in the formula, thetaiIs a parameter of the distribution function, i.e. the mean or variance; x is an explanatory variable; f is a parameter θiAnd an interpretation variable x;
step 3.2, the calculation formula of the AIC information criterion is as follows:
AIC＝2n-2ln(L) (3)；
where t is the number of parameters and L is the likelihood function, assuming that the error of the model follows an independent normal distribution.
The specific process of the step 4 is as follows: selecting an optimal probability distribution function from several standby probability distribution functions under the conditions of consistency and non-consistency of the relation between coverage and rainfall for each planting by adopting the minimum AIC criterion according to the calculation result of the step 3, wherein the specific formula is as follows:
Figure BDA0003420866670000051
wherein f (x) is an optimum distribution function; n is the number of spare distribution functions；
Figure BDA0003420866670000052
Is the AIC value calculated by the ith back-up distribution function from the sample values in the GAMLSS model.
The specific process of the step 5 is as follows: on the basis of the calculation in the step 4, selecting an optimal probability distribution function from a plurality of optimal probability distribution functions of the scenes with consistent relation and inconsistent relation between the vegetation coverage and the precipitation as a relation model between the vegetation coverage and the precipitation by adopting an AIC minimum criterion;
Figure BDA0003420866670000053
wherein GALSS (x) is the optimal GALSS model; m is the number of consistent and inconsistent scenes of the relationship between vegetation coverage and precipitation,
Figure BDA0003420866670000054
and calculating the AIC value of the jth vegetation coverage and precipitation relation consistency and non-consistency scene in the GALSS model according to the sample values.
The specific process of the step 6 is as follows: comparing the AIC value of the relation model between the vegetation coverage and the rainfall selected in the step 5 with the AIC value of the optimal probability distribution function model under the condition that the vegetation coverage and the rainfall are consistent, identifying whether the relation between the vegetation coverage and the rainfall is inconsistent or not, and calculating the reduction percentage spatial distribution of the AIC value under the condition that the optimal model and the consistent optimal distribution function exist;
Figure BDA0003420866670000055
in the formula, AICGAMLSS(x)The vegetation coverage and precipitation relation is an AIC value calculated according to the sample value under the optimal GALSS model;
Figure BDA0003420866670000061
is a scene with consistent relation between vegetation coverage and precipitationCalculating the next AIC value according to the sample value;
Figure BDA0003420866670000062
in the formula, RateAICThe vegetation coverage and precipitation relation is the AIC value calculated by the sample value under the optimal GALSS model and the AIC value reduction percentage calculated by the consistency model according to the sample value;
Figure BDA0003420866670000063
the AIC value is calculated according to the sample value under the condition that the relation between the vegetation coverage and precipitation is consistent; the relation between the vegetation coverage and the precipitation is an AIC value calculated according to the sample value under the optimal GALSS model.
The specific process of the step 7 is as follows: and 6, judging the spatial distribution of the non-uniform relation type between the vegetation coverage and the precipitation according to the calculation result of the step 6:
Figure BDA0003420866670000064
in the formula, AICGAMLSS(x)The vegetation coverage and precipitation relation is an AIC value calculated according to the sample value under the optimal GALSS model;
Figure BDA0003420866670000065
the AIC value is calculated according to the sample value under the condition that the relation between the vegetation coverage and precipitation is consistent; mu.sGAMLSS(x)The method is based on the sample mean value under the GALSS model with the optimal vegetation coverage and precipitation relation; deltaGAMLSS(x)The vegetation coverage and precipitation relation is the optimal GALSS model according to the sample variance; f (x) is a function of the mean or variance and an explanatory variable.
The invention has the beneficial effects that: the method for identifying the inconsistency of the regional vegetation coverage and the rainfall relation based on the GALSS model is accurate in calculation result and simple in calculation method, and can quickly and effectively diagnose and identify the inconsistency of the regional vegetation coverage and the rainfall in space distribution.
Drawings
FIG. 1 is a schematic diagram of the result of an optimal distribution function of a GALSS model of vegetation coverage and precipitation of an uncertain river basin in the identification method of the inconsistency of the regional vegetation coverage and precipitation relations;
FIG. 2 is a schematic diagram of a spatial recognition result of whether the vegetation coverage and precipitation relation of the uncertain river basin is inconsistent in the method for identifying the inconsistency between the vegetation coverage and precipitation relation of the area;
FIG. 3 is a schematic diagram of the percentage reduction result of AIC of the uncertain river basin in the identification method for the inconsistency of the vegetation coverage and precipitation relation in the area.
Fig. 4 is a schematic diagram of the identification result of the type of the inconsistency between the vegetation coverage and the precipitation in the uncertain river basin in the identification method for the inconsistency between the vegetation coverage and the precipitation in the area.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention discloses a method for identifying the inconsistency of regional vegetation coverage and precipitation relation, which is implemented according to the following steps:
step 1, determining a research area, determining the research area, determining a research period, and then collecting annual precipitation amount and annual NDVI grid data of a meteorological station or a rainfall station in the area;
step 2, carrying out interpolation according to the collected annual precipitation data of the meteorological stations in the research area and the peripheral area of the research area to obtain annual precipitation raster data with the same spatial resolution as the NDVI data, and meanwhile calculating according to the NDVI raster data to obtain vegetation coverage raster data;
according to the collected weather station annual precipitation data of the research area and the surrounding area, Kriging interpolation is carried out by utilizing ArcGIS software to obtain annual precipitation Raster data which is the same as the Spatial resolution of NDVI data, meanwhile, vegetation coverage Raster data are obtained by calculating through an ArcGIS software Spatial analysis Tools module Map Algebra tool router Calculator according to the NDVI Raster data, the vegetation coverage data are obtained by calculating through NDVI data through a maximum and minimum value method, and the formula is as follows:
f＝(NDVI-NDVImin)/(NDVImax-NDVImin)
wherein f is the vegetation coverage, NDVI is the vegetation index of the pixel, NDVImaxAnd NDVIminRespectively, the maximum and minimum values of the NDVI of the study region.
Step 3, constructing a plurality of possible scenes of consistency and non-consistency of the regional vegetation coverage and the rainfall relation based on a GALSS model according to the annual vegetation coverage grid data and the annual rainfall grid data obtained in the step 2, selecting a plurality of standby probability distribution functions as distribution functions of explained variables in the model under each scene, and respectively calculating the AIC value of the vegetation coverage and the rainfall relation model under each standby probability distribution function under each scene;
step 3.1, the distribution function of the interpreted variables in the GALSS model can be set to normal distribution (NO), exponential distribution (EXP), Weber distribution (WEI), etc., and in general, we only consider the first two parameters of the distribution function, i.e. mean and variance, and the two parameters of the distribution function can be set to four scenarios: (a) both the mean and the variance are unchanged; (b) the mean value changes with the change of the explanatory variable, and the variance does not change; (c) the mean is unchanged, and the variance changes with the change of the explanatory variable; (d) both mean and variance vary with explanatory variable; the expression of the parameters and explanatory variables is as follows:
θi＝f(x)
in the formula, thetaiIs a parameter of the distribution function, i.e. the mean or variance; x is an explanatory variable; f is a parameter θiThe functional relationship with the interpretation variable x may be generally set to be a linear function, an exponential function, a logarithmic function, a polynomial function, or the like.
Step 3, 2, an AIC information criterion calculation formula is as follows:
AIC＝2n-2ln(L)
wherein: t is the number of parameters and L is the likelihood function. The assumption is that the error of the model follows an independent normal distribution.
And 4, selecting an optimal probability distribution function from several standby probability distribution functions under the condition of consistency and inconsistency of the relation between coverage and rainfall of each planting by adopting an AIC minimum criterion according to the calculation result of the step 3, wherein the specific formula is as follows:
Figure BDA0003420866670000091
wherein f (x) is an optimum distribution function; n is the number of spare distribution functions;
Figure BDA0003420866670000092
is the AIC value calculated by the ith back-up distribution function from the sample values in the GAMLSS model.
Step 5, on the basis of the calculation in the step 4, selecting an optimal probability distribution function from a plurality of optimal probability distribution functions of the scenes with consistent and inconsistent relation between the vegetation coverage and the rainfall by adopting an AIC minimum criterion as a relation model between the vegetation coverage and the rainfall;
Figure BDA0003420866670000093
wherein GALSS (x) is the optimal GALSS model; m is the number of consistent and inconsistent scenes of the relationship between the vegetation coverage and the precipitation, and is a constant of 4;
Figure BDA0003420866670000094
and calculating the AIC value of the jth vegetation coverage and precipitation relation consistency and non-consistency scene in the GALSS model according to the sample values.
Step 6, comparing the AIC value of the relation model between the vegetation coverage and the precipitation optimized in the step 5 with the AIC value of the optimal probability distribution function model under the condition that the vegetation coverage and the precipitation are consistent, identifying whether the relation between the vegetation coverage and the precipitation has non-consistency, and calculating the spatial distribution of the reduction percentage of the AIC value under the condition that the optimal model and the consistent optimal distribution function exist;
Figure BDA0003420866670000101
in the formula, AICGAMLSS(x)The vegetation coverage and precipitation relation is an AIC value calculated according to the sample value under the optimal GALSS model;
Figure BDA0003420866670000102
the AIC value is calculated according to the sample value under the condition that the relation between vegetation coverage and precipitation is consistent (mean value and variance are not changed).
Figure BDA0003420866670000103
In the formula, RateAICThe vegetation coverage and precipitation relation is the AIC value calculated by the sample value under the optimal GALSS model and the AIC value reduction percentage calculated by the consistency model according to the sample value;
Figure BDA0003420866670000104
the AIC value is calculated according to the sample value under the condition that the relation between vegetation coverage and precipitation is consistent (mean value and variance are not changed); the relation between the vegetation coverage and the precipitation is an AIC value calculated according to the sample value under the optimal GALSS model.
And 7, judging the spatial distribution of the non-uniform relation type between the vegetation coverage and the precipitation according to the calculation result of the step 6.
Figure BDA0003420866670000105
In the formula, AICGAMLSS(x)The vegetation coverage and precipitation relation is an AIC value calculated according to the sample value under the optimal GALSS model;
Figure BDA0003420866670000111
the AIC value is calculated according to the sample value under the condition that the relation between vegetation coverage and precipitation is consistent (mean value and variance are not changed); mu.sGAMLSS(x)Is the basis of the GALSS model with the optimal vegetation coverage and precipitation relationA sample mean value; deltaGAMLSS(x)The vegetation coverage and precipitation relation is the optimal GALSS model according to the sample variance; f (x) is a function of the mean or variance and an explanatory variable, and may be generally set as a linear function, an exponential function, a logarithmic function, a polynomial function, or the like.
Examples
Taking an undefined river basin as an example, the method is implemented according to the following steps:
step 1, collecting basic data:
(1) year NDVI raster data with the resolution of 500 meters year by year in the uncertain river basin in 2000-2013 are downloaded from geospatial data cloud;
(2) the method comprises the following steps of determining the river basin and the areas nearby the river basin in 2000-2013, determining the edge, Suidede, Yanan, Lishi, Yulin Xingxian county, No. 38576, and annual precipitation data of 11 meteorological sites of county, Ettoke flag, Yanshan and Wuqi;
step 2, performing Kriging interpolation by utilizing ArcGIS software according to the collected annual precipitation data of the meteorological station of the non-fixed river basin and the surrounding area thereof to obtain precipitation Raster data with the annual resolution of 500 meters, which is the same as the Spatial resolution of the NDVI data, and calculating by utilizing an ArcGIS software Spatial analysis Tools module Map Algebra to obtain vegetation coverage Raster data according to the NDVI Raster data, wherein the vegetation coverage data are obtained by calculating the NDVI data by adopting a maximum and minimum value method, and the formula is as follows:
f＝(NDVI-NDVImin)/(NDVImax-NDVImin)
wherein f is the vegetation coverage, NDVI is the vegetation index of the pixel, NDVImaxAnd NDVIminRespectively, the maximum and minimum values of the NDVI of the study region.
Step 3, constructing a plurality of possible scenes of consistency and non-consistency of the regional vegetation coverage and the rainfall relation based on a GALSS model according to the annual vegetation coverage grid data and the annual rainfall grid data obtained in the step 2, selecting a plurality of standby probability distribution functions as distribution functions of explained variables in the model under each scene, and respectively calculating the AIC value of the vegetation coverage and the rainfall relation model under each standby probability distribution function under each scene;
step 3.1, setting the spare distribution function of the explained variables in the GALSS model into 6 types, such as Gunn-Bell distribution (GU), Weber distribution (WEI), gamma distribution (Gamma), logistic distribution (LO), exponential distribution (EXP), normal distribution (NO), and the like, and setting the possible situations of consistency and inconsistency of vegetation coverage and precipitation relation into 4 types: (1) both the mean and the variance are unchanged; (2) the mean value changes with the change of the explanatory variable, and the variance does not change; (3) the mean is unchanged, and the variance changes with the change of the explanatory variable; (4) both mean and variance vary with explanatory variable; the expression of the parameters and explanatory variables is as follows:
θi＝f(x)
in the formula, thetaiIs a parameter of the vegetation coverage distribution function, i.e. the mean or variance; x is an explanatory variable, i.e. precipitation; f is a parameter θiAnd an explanatory variable x, here set as a linear function.
Step 3.2, the calculation formula of the AIC information criterion is as follows:
AIC＝2n-2ln(L)
wherein: t is the number of parameters and L is the likelihood function. The assumption is that the error of the model follows an independent normal distribution.
And 4, selecting an optimal probability distribution function from several standby probability distribution functions under the condition of consistency and inconsistency of the relation between coverage and rainfall of each planting according to the calculation result of the step 3 by adopting an AIC minimum criterion, wherein the specific formula is as follows:
Figure BDA0003420866670000131
wherein f (x) is an optimum distribution function; n is the number of spare distribution functions, here 6;
Figure BDA0003420866670000132
is the AIC value calculated by the ith back-up distribution function from the sample values in the GAMLSS model.
The calculation results are shown in fig. 1.
Step 5, on the basis of the calculation in the step 4, selecting an optimal probability distribution function from a plurality of optimal probability distribution functions of the scenes with consistent and inconsistent relation between the vegetation coverage and the rainfall by adopting an AIC minimum criterion as a relation model between the vegetation coverage and the rainfall;
Figure BDA0003420866670000133
wherein GALSS (x) is the optimal GALSS model; m is the number of consistent and inconsistent scenes of the relationship between the vegetation coverage and the precipitation, and is a constant of 4;
Figure BDA0003420866670000134
and calculating the AIC value of the jth vegetation coverage and precipitation relation consistency and non-consistency scene in the GALSS model according to the sample values.
Step 6, comparing the AIC value of the relation model between the vegetation coverage and the precipitation optimized in the step 5 with the AIC value of the optimal probability distribution function model under the condition that the vegetation coverage and the precipitation are consistent, identifying whether the relation between the vegetation coverage and the precipitation has non-consistency, and calculating the spatial distribution of the reduction percentage of the AIC value under the condition that the optimal model and the consistent optimal distribution function exist;
Figure BDA0003420866670000141
in the formula, AICGAMLSS(x)The vegetation coverage and precipitation relation is an AIC value calculated according to the sample value under the optimal GALSS model;
Figure BDA0003420866670000142
the AIC value is calculated according to the sample value under the condition that the relation between vegetation coverage and precipitation is consistent (mean value and variance are not changed). The calculation results are shown in fig. 2.
Figure BDA0003420866670000143
In the formula, RateAICThe vegetation coverage and precipitation relation is the AIC value calculated by the sample value under the optimal GALSS model and the AIC value reduction percentage calculated by the consistency model according to the sample value;
Figure BDA0003420866670000144
the AIC value is calculated according to the sample value under the condition that the relation between vegetation coverage and precipitation is consistent (mean value and variance are not changed); the relation between the vegetation coverage and the precipitation is an AIC value calculated according to the sample value under the optimal GALSS model. The calculation results are shown in fig. 3.
And 7, judging the spatial distribution of the non-uniform relation type between the vegetation coverage of the area and the precipitation according to the calculation result of the step 6.
Figure BDA0003420866670000145
In the formula, AICGAMLSS(x)The vegetation coverage and precipitation relation is an AIC value calculated according to the sample value under the optimal GALSS model;
Figure BDA0003420866670000146
the AIC value is calculated according to the sample value under the condition that the relation between vegetation coverage and precipitation is consistent (mean value and variance are not changed); mu.sGAMLSS(x)The method is based on the sample mean value under the GALSS model with the optimal vegetation coverage and precipitation relation; deltaGAMLSS(x)The vegetation coverage and precipitation relation is the optimal GALSS model according to the sample variance; f (x) is a function of the mean or variance and an explanatory variable, one set to a linear function. The calculation results are shown in fig. 4.
The method for identifying the inconsistency of the regional vegetation coverage and the precipitation relation based on the GALSS model can accurately and effectively identify the inconsistency spatial distribution condition and the type of the inconsistency between the regional vegetation coverage and the precipitation, and provides scientific basis for regional ecological construction and restoration.

Claims (7)

1. The method for identifying the inconsistency of the regional vegetation coverage and precipitation relation is characterized by comprising the following steps of: the method specifically comprises the following steps:
step 1, determining a research area, and collecting annual NDVI grid data of the research area and annual water lowering data of a meteorological station;
step 2, carrying out interpolation according to the collected annual precipitation data of the meteorological stations in the research area and the area surrounding the area to obtain annual precipitation raster data with the same spatial resolution as the NDVI data, and meanwhile calculating according to the NDVI raster data to obtain vegetation coverage raster data;
step 3, constructing a plurality of possible scenes of consistency and non-consistency of the regional vegetation coverage and the rainfall relation based on a GALSS model according to the annual vegetation coverage grid data and the annual rainfall grid data obtained in the step 2, selecting a plurality of standby probability distribution functions as distribution functions of explained variables in the model under each scene, and respectively calculating the AIC value of the vegetation coverage and the rainfall relation model under each standby probability distribution function under each scene;
step 4, selecting an optimal probability distribution function from several standby probability distribution functions under the condition of consistency and inconsistency of the relation between coverage and rainfall of each planting by adopting the minimum AIC criterion according to the calculation result of the step 3;
step 5, on the basis of the calculation in the step 4, selecting an optimal probability distribution function from a plurality of optimal probability distribution functions of the scenes with consistent and inconsistent relation between the vegetation coverage and the rainfall by adopting an AIC minimum criterion as a relation model between the vegetation coverage and the rainfall;
step 6, comparing the AIC value of the relation model between the vegetation coverage and the precipitation selected in the step 5 with the AIC value of the optimal probability distribution function model under the condition that the vegetation coverage and the precipitation are consistent, and identifying whether the relation between the vegetation coverage and the precipitation is inconsistent or not;
and 7, judging the spatial distribution of the non-uniform relation type between the vegetation coverage of the area and the precipitation according to the calculation result of the step 6.
2. The method of claim 1, wherein the method comprises the steps of: the specific process of the step 2 is as follows:
the method comprises the following steps of utilizing ArcGIS software to carry out Kriging interpolation to obtain annual precipitation Raster data which are the same as the Spatial resolution of NDVI data, meanwhile, obtaining vegetation coverage Raster data through calculation of a Map Algebra tool router according to the NDVI Raster data through an ArcGIS software Spatial analysis Tools module, wherein the vegetation coverage Raster data are obtained through calculation of the NDVI data by adopting a maximum and minimum value method, and the formula is as follows:
f＝(NDVI-NDVImin)/(NDVImax-NDVImin) (1)；
wherein f is the vegetation coverage, NDVI is the vegetation index of the pixel, NDVImaxAnd NDVIminRespectively, the maximum and minimum values of the NDVI of the study region.
3. The method of claim 1, wherein the method comprises the steps of: the specific process of the step 3 is as follows:
the GALSS model sets the distribution function of the interpreted variables to normal distribution, exponential distribution, Weber distribution, and the mean and variance of the above distribution function are set to four scenarios: (a) both the mean and the variance are unchanged; (b) the mean value changes with the change of the explanatory variable, and the variance does not change; (c) the mean is unchanged, and the variance changes with the change of the explanatory variable; (d) the mean and variance both vary with the interpretive variable, and the parameters and interpretive variables are expressed as follows:
θi＝f(x) (2)；
in the formula, thetaiIs a parameter of the distribution function, i.e. the mean or variance; x is an explanatory variable; f is a parameter θiAnd an interpretation variable x;
step 3.2, the calculation formula of the AIC information criterion is as follows:
AIC＝2n-2ln(L) (3)；
where t is the number of parameters and L is the likelihood function, assuming that the error of the model follows an independent normal distribution.
4. The method of claim 1, wherein the method comprises the steps of: the specific process of the step 4 is as follows: selecting an optimal probability distribution function from several standby probability distribution functions under the conditions of consistency and non-consistency of the relation between coverage and rainfall for each planting by adopting the minimum AIC criterion according to the calculation result of the step 3, wherein the specific formula is as follows:
Figure FDA0003420866660000031
wherein f (x) is an optimum distribution function; n is the number of spare distribution functions;
Figure FDA0003420866660000033
is the AIC value calculated by the ith back-up distribution function from the sample values in the GAMLSS model.
5. The method of claim 4, wherein the method comprises the steps of: the specific process of the step 5 is as follows: on the basis of the calculation in the step 4, selecting an optimal probability distribution function from a plurality of optimal probability distribution functions of the scenes with consistent relation and inconsistent relation between the vegetation coverage and the precipitation as a relation model between the vegetation coverage and the precipitation by adopting an AIC minimum criterion;
Figure FDA0003420866660000032
wherein GALSS (x) is the optimal GALSS model; m is the number of consistent and inconsistent scenes of the relationship between vegetation coverage and precipitation,
Figure FDA0003420866660000034
and calculating the AIC value of the jth vegetation coverage and precipitation relation consistency and non-consistency scene in the GALSS model according to the sample values.
6. The method of claim 5, wherein the method comprises the steps of: the specific process of the step 6 is as follows: comparing the AIC value of the relation model between the vegetation coverage and the rainfall selected in the step 5 with the AIC value of the optimal probability distribution function model under the condition that the vegetation coverage and the rainfall are consistent, identifying whether the relation between the vegetation coverage and the rainfall is inconsistent or not, and calculating the reduction percentage spatial distribution of the AIC value under the condition that the optimal model and the consistent optimal distribution function exist;
Figure FDA0003420866660000041
in the formula, AICGAMLSS(x)The vegetation coverage and precipitation relation is an AIC value calculated according to the sample value under the optimal GALSS model;
Figure FDA0003420866660000043
the AIC value is calculated according to the sample value under the condition that the relation between the vegetation coverage and precipitation is consistent;
Figure FDA0003420866660000042
in the formula, RateAICThe vegetation coverage and precipitation relation is the AIC value calculated by the sample value under the optimal GALSS model and the AIC value reduction percentage calculated by the consistency model according to the sample value;
Figure FDA0003420866660000044
the AIC value is calculated according to the sample value under the condition that the relation between the vegetation coverage and precipitation is consistent; the relation between the vegetation coverage and the precipitation is an AIC value calculated according to the sample value under the optimal GALSS model.
7. The method of claim 6, wherein the method comprises the steps of: the specific process of the step 7 is as follows: and 6, judging the spatial distribution of the non-uniform relation type between the vegetation coverage and the precipitation according to the calculation result of the step 6:
Figure FDA0003420866660000051
in the formula, AICGAMLSS(x)The vegetation coverage and precipitation relation is an AIC value calculated according to the sample value under the optimal GALSS model;
Figure FDA0003420866660000052
the AIC value is calculated according to the sample value under the condition that the relation between the vegetation coverage and precipitation is consistent; mu.sGAMLSS(x)The method is based on the sample mean value under the GALSS model with the optimal vegetation coverage and precipitation relation; deltaGAMLSS(x)The vegetation coverage and precipitation relation is the optimal GALSS model according to the sample variance; f (x) is a function of the mean or variance and an explanatory variable.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116663392A (en) * 2023-04-23 2023-08-29 中国水利水电科学研究院 Method, device, equipment and medium for simulating two-dimensional composite event of hydrological disaster

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116663392A (en) * 2023-04-23 2023-08-29 中国水利水电科学研究院 Method, device, equipment and medium for simulating two-dimensional composite event of hydrological disaster
CN116663392B (en) * 2023-04-23 2024-02-20 中国水利水电科学研究院 Method, device, equipment and medium for simulating two-dimensional composite event of hydrological disaster

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