CN114723330A - Vegetation change influence factor evaluation method based on structural equation model - Google Patents
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Abstract
The invention discloses a vegetation change influence factor evaluation method based on a structural equation model. The method mainly comprises the following steps: (1) collecting NDVI data and an NDVI variation impact factor data set; (2) preprocessing the data; (3) establishing a conceptualized structural equation model and inputting the processed variables into the model; (4) calculating a goodness-of-fit index of the model; (5) correcting the model; (6) finally, the influence mechanism of vegetation change is obtained, and the result is analyzed. The method realizes quantitative evaluation of the influence factors of vegetation change, effectively quantifies direct, indirect and total influence of each influence factor on vegetation change, and provides scientific basis for regional ecological environment protection and decision management.
Description
Technical Field
The invention relates to the field of vegetation, in particular to a vegetation change influence factor evaluation method based on a structural equation model.
Background
Vegetation growth is strongly influenced by terrain, climate change and human activities, and vegetation coverage changes affect the global or regional ecological environment. The space-time change of the vegetation in the area is known, the response condition of the vegetation in the area to each influencing factor is explored, and theoretical support can be provided for the ecological environment protection of the area.
The remote sensing technology is an effective means for realizing vegetation monitoring with long time sequence and large spatial scale. The Normalized Difference Vegetation Index (NDVI) can accurately reflect the coverage of surface vegetation, is the most common index for evaluating the growth condition of vegetation, and the annual maximum value of the index can effectively reflect the optimal condition of annual growth of vegetation. Most studies focus on the direct effect of independent variables on dependent variables, ignoring indirect effects, and thus producing biased results.
Therefore, the direct and indirect influence of each influencing factor on vegetation coverage change is quantified, the total influence of each influencing factor on vegetation change is obtained, and the method has important significance on regional ecological environment protection and decision governance.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a vegetation change influence factor evaluation method based on a structural equation model, so that the direct, indirect and total influences of all influence factors on vegetation change are obtained.
The purpose of the invention can be realized by the following technical scheme: a vegetation change influence factor evaluation method based on a structural equation model comprises the following steps:
s1, data collection: collecting downloaded NDVI data; collecting and downloading an influence factor data set influencing the NDVI change;
s2, preprocessing data: the method comprises one or more items of splicing and cutting of data, projection conversion of data, reclassification of data, resampling of data and extraction of sampling points;
s3, processing the factor selected in the step S1 in a step S2, and using the factor as an input variable of the structural equation model;
s4, establishing a conceptualized structural equation model, inputting the variables obtained in the step S3 into the model, and calculating each goodness-of-fit index of the model;
s5, judging whether the goodness-of-fit index of each model calculated in the step S4 meets the requirement or not according to the goodness-of-fit index adaptation standard; if yes, judging main influence factors of the NDVI change according to the significance and the fitted standardized path coefficient, judging the influence direction of each influence factor on the NDVI change according to the sign of the standardized path coefficient, and judging the direct and indirect influence of each influence factor on the NDVI change according to the path relation from the independent variable to the dependent variable to obtain the total influence of each influence factor on the NDVI change; otherwise, the model is modified in two ways: 1) correcting the model through a model correction value, 2) deleting the insignificant variable and the path to correct the model, and repeating the step S4;
and S6, analyzing the NDVI change influence factors according to the finally obtained structural equation model to obtain a quantitative analysis result of the NDVI change influence factors.
Further, the influence factor data set of step S1 includes: terrain factors, air temperature factors, precipitation factors, human activity factors; the terrain factor is measured by two indexes of elevation and gradient, and the human activity factor is measured by three indexes of night light, population density and land utilization.
Further, in step S3, according to the influence factor data set selected in step S1, through the processing in step S2, the obtained input variables are specifically:
extracting an elevation value of each sampling point;
extracting the gradient value of each sampling point;
extracting the slope of the air temperature change of each sampling point;
extracting the slope of rainfall change of each sampling point;
extracting the slope of the night lamplight change of each sampling point;
extracting the slope of population density change of each sampling point;
extracting the value of each sampling point according to whether the land use type changes;
the slope of the NDVI change is extracted for each sample point.
Further, in step S5, when the model goodness of fit index calculated in step S4 meets the requirement, a path relationship between the terrain, the air temperature, the precipitation, the human activity, and the NDVI change is obtained, the main influence factor of the NDVI change is determined according to the significance and the normalized path coefficient, the influence direction, i.e., the positive influence and the negative influence, of each influence factor on the NDVI change is determined according to the sign of the normalized path coefficient, and the influence mechanism of each influence factor on the NDVI change is obtained according to the path relationship from the independent variable to the dependent variable, so that the direct and indirect influence of each influence factor on the NDVI change is determined, and the total influence of each influence factor on the NDVI change is obtained.
Further, in step S5, the modifying the model according to the model modification value and the manner of deleting the insignificant variable and the path specifically includes:
1) arranging the model correction values in a descending order, and sequentially establishing correlation relations among variables according to the order, thereby correcting the structural equation model;
2) and deleting the insignificant variable or the path according to the significance test result of each path, thereby correcting the model.
The invention has the beneficial effects that: according to the method, vegetation change influence factors are selected and serve as input variables of a structural equation model through pretreatment, iterative analysis is carried out on all the variables through the structural equation model, the model is corrected in two modes of establishing correlation among the variables and deleting unremarkable variables and paths according to the sequence from large to small of the correction value, so that the influence degree, the influence direction and the influence mode of all the influence factors on vegetation change are obtained, the direct and indirect influences of all the influence factors on vegetation change are effectively quantified, the total influence of all the influence factors on vegetation change is obtained, and scientific basis is further provided for regional ecological environment construction and sustainable development.
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The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method for evaluating vegetation change influencing factors based on a structural equation model according to the present invention;
FIG. 2 is a schematic diagram of a conceptualized structural equation model established in an embodiment of the present invention;
FIG. 3 is a graph of the results of the model of the initial structural equation in an embodiment of the present invention;
FIG. 4 is a graph of the normalized path of the finally fitted structural equation model in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A vegetation change influence factor evaluation method based on a structural equation model comprises the following steps:
s1, data collection: collecting downloaded NDVI data; collecting and downloading an influence factor data set influencing the NDVI change;
s2, preprocessing data: the method comprises one or more items of data splicing and cutting, data projection conversion, data reclassification, data resampling and sampling point extraction;
s3, processing the factor selected in the step S1 in a step S2, and using the factor as an input variable of the structural equation model;
s4, establishing a conceptualized structural equation model, inputting the variables obtained in the step S3 into the model, and calculating each goodness-of-fit index of the model;
s5, judging whether the goodness-of-fit index of each model calculated in the step S4 meets the requirement or not according to the goodness-of-fit index adaptation standard; if yes, judging main influence factors of the NDVI change according to the significance and the fitted standardized path coefficient, judging the influence direction of each influence factor on the NDVI change according to the sign of the standardized path coefficient, and judging the direct and indirect influence of each influence factor on the NDVI change according to the path relation from the independent variable to the dependent variable to obtain the total influence of each influence factor on the NDVI change; otherwise, the model is modified in two ways: 1) correcting the model through a model correction value, 2) deleting the insignificant variable and the path to correct the model, and repeating the step S4;
and S6, analyzing the NDVI change influence factors according to the finally obtained structural equation model to obtain a quantitative analysis result of the NDVI change influence factors.
Further, the influence factor data set of step S1 includes: terrain factors, air temperature factors, precipitation factors, human activity factors; the terrain factor is measured by two indexes of elevation and gradient, and the human activity factor is measured by three indexes of night light, population density and land utilization.
Further, in step S3, according to the influence factor data set selected in step S1, through the processing in step S2, the obtained input variables are specifically:
extracting an elevation value of each sampling point;
extracting the gradient value of each sampling point;
extracting the slope of the air temperature change of each sampling point;
extracting the slope of rainfall change of each sampling point;
extracting the slope of the night lamplight change of each sampling point;
extracting the slope of population density change of each sampling point;
extracting the value of each sampling point according to whether the land use type changes;
the slope of the NDVI change is extracted for each sample point.
Further, in step S5, when the model goodness of fit index calculated in step S4 meets the requirement, a path relationship between the terrain, the air temperature, the precipitation, the human activity, and the NDVI change is obtained, the main influence factor of the NDVI change is determined according to the significance and the normalized path coefficient, the influence direction, i.e., the positive influence and the negative influence, of each influence factor on the NDVI change is determined according to the sign of the normalized path coefficient, and the influence mechanism of each influence factor on the NDVI change is obtained according to the path relationship from the independent variable to the dependent variable, so that the direct and indirect influence of each influence factor on the NDVI change is determined, and the total influence of each influence factor on the NDVI change is obtained.
Further, in step S5, the modifying the model according to the model modification value and the manner of deleting the insignificant variable and the path specifically includes:
1) arranging the model correction values in a descending order, and sequentially establishing correlation relations among variables according to the order, thereby correcting the structural equation model;
2) and deleting the insignificant variable or the path according to the significance test result of each path, thereby correcting the model.
In a specific embodiment, the embodiment of the invention provides a vegetation change influence factor evaluation method based on a structural equation model by taking Anhui province as a case area and taking 2000-2018 as a research period, and specifically comprises the following steps:
s1, data collection: collecting downloaded NDVI data and an influence factor data set of NDVI changes;
MOD13Q1 NDVI, SRTM DEM data are downloaded based on the Google Earth Engine (GEE) cloud platform. The spatial resolution of the MOD13Q1 NDVI data is 250m, and the temporal resolution is 16 d; the SRTM DEM data has a spatial resolution of 30 m.
Population density data from WorldPop (https://www.worldpop.org/) And the spatial resolution of the published world population density map is 1 km.
The night light data is from an extended time series of global NPP-VIIRS night light data based on cross-sensor calibration (2000 and 2018), with a data set spatial resolution of 500 m.
The land utilization data comes from resource and environmental science data center of Chinese academy of sciences: (http://www.resdc.cn) The land utilization data set of Anhui province is provided, the data set comprises 6 primary classes and 25 secondary classes of cultivated land, forest land, grassland, water areas, residential areas, unused land and the like, and the spatial resolution is 1 km.
Climate data (annual average temperature and annual precipitation) from ChinaWeather data network (http://data.cma.cn/) The Chinese ground air temperature month value is 0.5 degree multiplied by 0.5 degree lattice point data set (V2.0) and the Chinese ground precipitation month value is 0.5 degree multiplied by 0.5 degree lattice point data set (V2.0). Data preparation see table 1:
TABLE 1 data introduction Table
S2, preprocessing data: the method comprises one or more items of data splicing and cutting, data projection conversion, data reclassification, data resampling and sampling point extraction;
extracting elevation and gradient information according to DEM data;
all factors required by the case are clipped with the case area as a boundary, all data are projected to a WGS _1984_ UTM _ Zone _50N coordinate system in ArcGIS, and resampled to the same spatial resolution (250m) as NDVI based on a nearest neighbor method to ensure data availability.
And calculating slopes of temperature equalization change, annual precipitation change, night light change, population density change and NDVI change in the year 2000-2018 pixel by pixel respectively based on a unitary linear regression method.
And (3) recording the pixel with the changed land use type in the 2000-plus 2018 case area as 0 and the pixel without the change as 1.
And removing the water body part by the case, taking the central point of a 3km multiplied by 3km grid, removing missing values, and extracting 14789 sampling points in total.
S3, processing the factor selected in the step S1 in a step S2, and using the factor as an input variable of the structural equation model;
extracting an elevation value of each sampling point;
extracting the gradient value of each sampling point;
extracting the slope of annual average temperature change of each sampling point;
extracting the slope of annual precipitation change of each sampling point;
extracting the slope of the night lamplight change of each sampling point;
extracting the slope of population density change of each sampling point;
extracting the value of each sampling point according to whether the land use type changes;
the slope of the NDVI change is extracted for each sample point.
S4, establishing a conceptualized structural equation model, inputting the variables obtained in the step S3 into the model, and calculating each goodness-of-fit index of the model;
in this embodiment, a conceptualized structural equation model is first established according to the NDVI variation and the influence factor data selected in the step S1, as shown in fig. 2, each factor obtained in the step S3 is used as an input variable of the structural equation model to perform iterative analysis, and each goodness-of-fit index of the model is obtained through calculation.
S5, judging whether the goodness-of-fit index of each model calculated in the step S4 meets the requirement or not according to the goodness-of-fit index adaptation standard; if yes, judging main influence factors of the NDVI change according to the significance and the fitted standardized path coefficient, judging the influence direction of each influence factor on the NDVI change according to the sign of the standardized path coefficient, and judging the direct and indirect influence of each influence factor on the NDVI change according to the path relation from the independent variable to the dependent variable to obtain the total influence of each influence factor on the NDVI change; otherwise, the model is modified in two ways:
1) the model is corrected by a model correction value,
2) deleting the insignificant variables and paths to modify the model, and repeating the step S4;
in this embodiment, the invention first compares each goodness-of-fit index obtained in step S4 with the goodness-of-fit index adaptation standard according to the goodness-of-fit index adaptation standard, and determines whether the model goodness-of-fit index calculated in step S4 meets the requirements.
If all the goodness-of-fit indexes of the structural equation model meet the adaptation standards of the goodness-of-fit indexes, observing the finally obtained structural equation model, judging main influence factors of NDVI change according to the significance and the magnitude of the fitted standardized path coefficients, judging the influence direction of all the influence factors on the NDVI change according to the signs of the standardized path coefficients, judging the direct and indirect influences of all the influence factors on the NDVI change according to the path relation from the independent variable to the dependent variable, and obtaining the total influence of all the influence factors on the NDVI change.
If each goodness-of-fit index of the structural equation model does not meet the fitting standard of the goodness-of-fit index, the model needs to be corrected, and two main ways exist: 1) and correcting the model through the model correction value, specifically arranging the model correction values in a descending order, establishing a correlation relation between two corresponding variables in the order, or deleting the variable with the larger correction value. 2) And deleting the insignificant variables and paths to modify the model, specifically deleting the insignificant paths according to the significance result of each path of the model. Repeating step S4;
in this embodiment, the present invention obtains the estimation result of the initial structural equation model shown in fig. 3 according to the initially designed conceptual structural equation model shown in fig. 2, and the goodness of fit indexes of the estimation result are shown in table 2.
TABLE 2 goodness of fit index results for the initial structural equation model
In table 2, χ 2/df is the chi-squared degree of freedom ratio, RMSEA is the approximate error root mean square, SRMR is the normalized root mean square residual, CFI is the comparative fit index, GFI is the goodness-of-fit index, and IFI is the modified standard fit index.
According to the goodness of fit indexes of the initial structure equation model obtained in table 2, χ 2/df of the model is 17.483 (> 3) which does not meet the fitting standard of the model, so that the model needs to be corrected.
The invention corrects the model by establishing a correlation relationship according to the model correction value from big to small, deleting the variable with big correction value or deleting the non-obvious variable and path.
The correction coefficients of the initial model are shown in table 3, and according to the method for correcting the model, e7 and e8 with the largest correction values can be selected to be correlated, or the variable corresponding to e7 can be deleted.
TABLE 3 initial structural equation model correction values
The significance results of the initial structural equation model path coefficients are shown in table 4, and as can be seen from table 4, the paths between the NDVI change and the annual temperature-average change, and the paths between the annual temperature-average change and the terrain are not significant.
TABLE 4 significance of initial structural equation model path coefficients
Furthermore, in order to improve the goodness of fit of the model, the method firstly deletes the annual average temperature change variable by combining the model correction value result. The goodness-of-fit indicators for the modified model are shown in table 5.
TABLE 5 goodness of fit index results for the first corrected model
Furthermore, as can be seen from table 5, though the model has a χ 2/df of 5.758 < 17.483, the fitting effect of the model is improved to a certain extent, χ 2/df is still > 3, and the fitting standard of the model is not satisfied, so that the model needs to be further corrected.
The correction values of the model after the first correction are shown in table 6, and the present invention establishes the correlation in the order of the model correction values from large to small according to the aforementioned model correction method.
TABLE 6 correction values of the model after first correction
Further, as can be seen from table 6, first, the correlation between e6 and e8 having the largest model correction value is established. The goodness-of-fit indicators for each of the modified models are shown in table 7.
TABLE 7 goodness of fit index results for the second revised model
As can be seen from table 7, the goodness of fit index of the model after the second correction is further improved, but the fitting standard of the model fitting index is still not satisfied, and therefore, the model needs to be further corrected.
After the model is corrected for multiple times according to the model correction method, the goodness-of-fit indexes of the finally-fitted structural equation model are shown in table 8.
TABLE 8 goodness of fit index results for each item of the finally fitted structural equation model
As can be seen from table 8, each goodness-of-fit index of the structural equation model after multiple corrections meets the adaptation standard of the model.
The normalized path coefficients and the significance of the obtained final fitted structural equation model are shown in table 9, where the magnitude of the normalized path coefficients represents the strength of the influence of the independent variables on the dependent variables, and the sign represents the influence direction of the independent variables on the dependent variables.
TABLE 9 normalized Path coefficients and significance of the finally fitted structural equation model
As can be seen from table 9, each path of the model passes a confidence test at a 0.001 level, where terrain has a significant negative impact not only on human activity changes (-0.14), but also on annual precipitation changes (0.52); changes in human activity and annual precipitation yield have a significant negative impact on NDVI changes (-0.47 and-0.04); terrain has a significant positive impact on NDVI variations (0.06).
And S6, analyzing the NDVI change influence factors according to the finally obtained structural equation model to obtain a quantitative analysis result of the NDVI change influence factors.
In this embodiment, according to the fitting result of step S5, the finally fitted structural equation model obtained by the present invention is shown in fig. 4.
As can be seen from fig. 4, the terrain has not only a direct effect on NDVI changes in anhui, but also an indirect effect, which are: 1) significant positive impact on NDVI changes; 2) significant positive effects on NDVI changes by inhibiting changes in human activity; 3) the NDVI change is significantly negatively impacted by promoting annual precipitation changes. Moreover, the elevation and the gradient can well measure the influence of the terrain on the NDVI change. In addition, changes in human activity and annual precipitation can only have a direct effect on NDVI changes, with human activity changes having a significant negative effect on NDVI changes, night light changes being a good measure of human activity changes, and annual precipitation changes having a significant negative effect on NDVI changes.
The direct and indirect influences of the influencing factors on the NDVI change are combined to obtain the total influence of the influencing factors on the NDVI change, and the results are shown in Table 10.
TABLE 10 Total Effect of various influencing factors on NDVI Change
As can be seen from Table 10, the change in human activity was the largest in the change in NDVI, and was the primary factor in the change in NDVI, with an influence coefficient of-0.47, a total influence of terrain on the change in NDVI of 0.11, and a total influence of annual precipitation change on the change in NDVI of-0.04.
The invention utilizes the structural equation model to carry out quantitative analysis on the influence factors of vegetation change to obtain the direct, indirect and total influence of each influence factor on the vegetation change, namely the influence direction, the influence degree and the influence mechanism of each influence factor on the vegetation change.
The above-mentioned embodiments are only for assisting the reader to understand the principle of the present invention, and are not intended to limit the present invention, and the data processing and the study area selection in the present invention can be determined according to the requirement. The present invention may be further modified and improved without departing from the spirit and scope of the present invention, and such modifications and improvements are intended to be within the scope of the present invention.
Claims (5)
1. A vegetation change influence factor evaluation method based on a structural equation model is characterized by comprising the following steps:
s1, data collection: collecting downloaded NDVI data; collecting a data set of impact factors that affect the change in NDVI, wherein NDVI is a normalized vegetation index;
s2, preprocessing data: the method comprises the following steps of splicing and cutting data, projecting and converting the data, reclassifying the data, resampling the data and extracting sampling points;
s3, processing according to the NDVI change influence factor selected in the step S1 through a step S2, and taking the NDVI change influence factor as an input variable of the structural equation model;
s4, establishing a conceptualized structural equation model, inputting the variables obtained in the step S3 into the model, and calculating each goodness-of-fit index of the model;
s5, judging whether the goodness-of-fit index of each model calculated in the step S4 meets the requirement or not according to the goodness-of-fit index adaptation standard; if yes, judging main influence factors of the NDVI change according to the significance and the fitted standardized path coefficient, judging the influence direction of each influence factor on the NDVI change according to the sign of the standardized path coefficient, and judging the direct and indirect influence of each influence factor on the NDVI change according to the path relation from the independent variable to the dependent variable to obtain the total influence of each influence factor on the NDVI change; otherwise, the model is modified in two ways: 1) correcting the model through a model correction value, 2) deleting the insignificant variable and the path to correct the model, and repeating the step S4;
and S6, analyzing the NDVI change influence factors according to the finally obtained structural equation model to obtain a quantitative analysis result of the NDVI change influence factors.
2. The method of claim 1, wherein the data set of influence factors in step S1 includes: terrain factors, air temperature factors, precipitation factors, human activity factors; the terrain factor is measured by two indexes of elevation and gradient, and the human activity factor is measured by three indexes of night light, population density and land utilization.
3. The method for evaluating vegetation change influence factors based on the structural equation model of claim 1, wherein the step S3 is performed according to the influence factor data set selected in the step S1, and the step S2 performs processing to obtain input variables specifically as follows:
extracting an elevation value of each sampling point;
extracting the gradient value of each sampling point;
extracting the slope of the air temperature change of each sampling point;
extracting the slope of rainfall change of each sampling point;
extracting the slope of the night lamplight change of each sampling point;
extracting the slope of population density change of each sampling point;
extracting the value of each sampling point according to whether the land use type changes;
the slope of the NDVI change is extracted for each sample point.
4. The method of claim 1, wherein in step S5, when the model goodness-of-fit index calculated in step S4 meets the requirement, the path relationship among terrain, air temperature, precipitation, human activities, NDVI changes, the main influencing factors and influencing directions of NDVI changes, the influencing mechanisms of the influencing factors on NDVI changes, and the total influence of the influencing factors on NDVI changes are obtained.
5. The method for evaluating vegetation change influence factors based on the structural equation model of claim 1, wherein in the step S5, the modification of the model specifically comprises:
1) arranging the model correction values in a descending order, and sequentially establishing correlation relations among variables according to the order, thereby correcting the structural equation model;
2) and deleting the insignificant variable or the path according to the significance test result of each path, thereby correcting the model.
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