CN111858700A - Loess gully development influence mechanism analysis method based on geographic detector - Google Patents
Loess gully development influence mechanism analysis method based on geographic detector Download PDFInfo
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Abstract
The invention discloses a loess gully development influence mechanism analysis method based on a geographic detector, which comprises the following steps of: the method comprises the steps of obtaining DEM data, valley density data, first-class environmental data and second-class environmental data in the loess plateau range, and preprocessing the obtained data to obtain raster data; converting the valley density grid data into a vector point data set, simultaneously recording the valley density value at each point position, and extracting a point data subset from the point data set according to an even distribution principle; reclassifying the second type of environment data; adding the classified data as attribute information into a point data subset attribute table; and exporting the attribute information of the point data subset to geographic detector software for geographic detection calculation. The invention analyzes various environmental factors influencing the development of loess gullies based on the geographic detector, finds the environmental factor influencing the development of the loess gullies to the maximum extent, and analyzes the development of the loess gullies more accurately and comprehensively.
Description
Technical Field
The invention relates to the technical field of loess gully development influence mechanisms, in particular to a loess gully development influence mechanism analysis method based on a geographic detector.
Background
The development problem of loess ravines is always a research hotspot in the field of geomorphology, and the current research mostly focuses on hydrodynamic mechanism research of loess ravine development and research of simulation evolution models of the loess ravine development, and rarely focuses on the influence of environmental factors in the loess ravine development process. In the loess gully development process, rainfall is the first motive force for driving gully erosion, but not the only factor, and gully formation and further development are the result of comprehensive action of multiple factors in nature, and are a nonlinear action process, so a nonlinear model is urgently needed to solve the problem. In the aspect of exploring the relevance among multiple variables, a spatial regression analysis method is used, the theoretical basis of the method is spatial autocorrelation, and research methods and tools such as SAR/MAR/CAR, least square method (OLS), heuristic regression model, geoweighted regression model (GWR) and the like appear in the field, and the methods facilitate the analysis and mining of data with spatial autocorrelation characteristics. However, the traditional regression analysis model has obvious defects in the aspect of detecting the interaction of the two factors on the dependent variable, and the statistical significance of the traditional regression analysis model cannot be guaranteed.
Disclosure of Invention
In order to overcome the above problems or at least partially solve the above problems, embodiments of the present invention provide a method for analyzing influence mechanism of loess gully development based on a geographic detector, which analyzes various environmental factors influencing loess gully development based on the geographic detector, so as to implement loess gully development environmental dependency analysis based on the geographic detector, establish a nonlinear relationship model between loess gully development and environmental factors, search influence degrees of different environmental factors in the loess gully development process, and find an environmental factor having the greatest influence on loess gully development, so as to perform more accurate and comprehensive analysis on loess gully development in the following process.
The embodiment of the invention is realized by the following steps:
a loess gully development influence mechanism analysis method based on a geographic detector comprises the following steps:
the method comprises the steps of obtaining DEM data, valley density data, first type environmental data and second type environmental data in the loess plateau range, and preprocessing the obtained DEM data, the valley density data, the first type environmental data and the second type environmental data to obtain raster data;
converting valley density raster data in the obtained raster data into a vector point data set, simultaneously recording the valley density value at each point position, and extracting a point data subset from the vector point data set according to an even distribution principle;
The method comprises the steps that raster data of first-class environment data in the raster data are reserved, and the first-class environment data are used as attribute information to be added into an attribute table of a point data subset;
reclassifying the second type of environment data in the raster data according to a natural discontinuity point classification method;
adding the reclassified second-class environment data serving as attribute information into an attribute table of the point data subset;
and exporting the attribute information in the attribute table of the point data subset to geographic detector software, performing geographic detection calculation, and acquiring a detection result.
Analyzing a loess gully development influence mechanism, acquiring a plurality of types of data such as DEM data, gully density data, first type of environmental data and second type of environmental data in a loess plateau range, providing comprehensive data for subsequent analysis, preprocessing the acquired data, uniformly researching a projection coordinate system of the data, uniformly researching the grid size of the data, calculating and acquiring gradient data and slope direction data according to the DEM data, acquiring the grid data of the DEM data, the gully density data, the first type of environmental data and the second type of environmental data, converting the acquired gully density grid data into a vector point data set, simultaneously recording the gully density value at each point position, extracting a principle point data subset from the vector point data set according to uniform distribution, and reserving the original type of the first type of environmental data (land utilization data, erosion intensity data, soil type data and vegetation type data), namely the grid data of the first type of environmental data, adding the first type of environment data serving as attribute information into an attribute table of the point data subset, reclassifying the raster data (gradient data, slope data, NDVI (normalized difference score) data, rainfall data, loess thickness data and soil texture data) of the second type of environment data in the raster data according to a natural breakpoint classification method, and classifying the second type of environment data into 30 types according to the natural breakpoint classification method; adding the reclassified second-class environment data serving as attribute information into a point data subset attribute table; and exporting the attribute information of the point data subset to geographic detector software, taking the valley density as a dependent variable and other environmental factors as independent variables, and performing geographic detection calculation to finally obtain risk detection, factor detection, ecological detection and interaction detection results. The method comprises the steps of analyzing various environmental factors influencing the development of the loess gully based on a geographic detector, realizing the dependency analysis of the loess gully development environment based on the geographic detector, establishing a nonlinear relation model between the loess gully development and the environmental factors, exploring the influence degrees of different environmental factors in the loess gully development process, and finding out the environmental factor which has the greatest influence on the loess gully development, so that the loess gully development can be analyzed more accurately and comprehensively in the following process.
In some embodiments of the present invention, a loess gully development influence mechanism analysis method based on a geographic detector includes the following steps of preprocessing acquired DEM data, gully density data, first type environmental data and second type environmental data to obtain grid data:
uniformly setting the projection coordinate system information of the acquired DEM data, the valley density data, the first type of environment data and the second type of environment data as a Mercator projection coordinate system, and acquiring initial raster data of the DEM data, the valley density data, the first type of environment data and the second type of environment data;
setting sampling grid size information;
and resampling the initial raster data of the acquired DEM data, the valley density data, the first type of environmental data and the second type of environmental data according to the set sampling raster size information to acquire target raster data.
In some embodiments of the present invention, a loess gully development influence mechanism analysis method based on a geographic detector, the method for preprocessing the acquired DEM data further includes the following steps:
and analyzing the acquired DEM data, and calculating and acquiring gradient data and slope data according to the DEM data.
In some embodiments of the present invention, a loess gully development influence mechanism analysis method based on a geographic detector, the first type of environmental data includes land utilization data, erosion intensity data, soil type data and vegetation type data; the second type of environmental data includes grade data, slope data, NDVI data, rainfall data, loess thickness data, and soil texture data.
In some embodiments of the present invention, a loess gully development influence mechanism analysis method based on a geographic detector, wherein soil texture data includes sandy soil content data, silt soil content data and clay content data.
In some embodiments of the present invention, a loess gully development influence mechanism analysis method based on a geographic detector, a method for extracting point data subsets from a point data set according to a uniform distribution principle includes the following steps:
a1, acquiring element information and element number N in the vector point data set;
a2, dividing the vector point data set into a first point data subset and a second point data subset, and distributing element information in the vector point data set into the first point data subset and the second point data subset according to a set proportion;
A3, acquiring the number L of elements in the first point data subset and the number N-L of elements in the second point data subset;
a4, generating a random value from the [0, 1] interval according to a uniform distribution principle, judging whether the random value is smaller than L/N, if so, distributing the pixel information to a first point data subset, and entering the step A5; if not, then assigning the pixel information to a second subset of point data, and proceeding to step A5;
a5, judging whether the element information in the vector point data set is distributed completely, if yes, outputting a first point data subset and a second point data subset; if not, step A4 is entered.
In some embodiments of the present invention, a loess ravine development influence mechanism analysis method based on a geographic detector, a method for reclassifying second-class environmental data according to a natural breakpoint classification method includes the following steps:
respectively importing the gradient data, the slope direction data, the NDVI data, the rainfall data, the loess thickness data and the soil texture data into a reclassification tool of ArcGIS software;
presetting classification categories according to a natural discontinuity point classification method, and reclassifying the imported data according to the classification categories; and marking the missing value generated in the re-classification process as NoData.
In some embodiments of the present invention, a loess ravine development influence mechanism analysis method based on a geographic detector, a method of adding reclassified second-class environmental data as attribute information to a point data subset attribute table includes the following steps:
and extracting the gradient data, the slope direction data, the NDVI data, the rainfall data, the loess thickness data and the soil texture data at each point position into a point data subset attribute table through a multi-value extraction to point tool in ArcGIS software.
In some embodiments of the present invention, a loess ravine development influence mechanism analysis method based on a geographic detector, the method of exporting the data subset attribute information to the geographic detector software includes the following steps:
opening a point data subset attribute table from ArcGIS software, and exporting attribute table information into a dbf format file;
processing the data in the dbf format file, eliminating abnormal values to obtain a new data attribute table and storing the new data attribute table as an xlsx format file;
the data in the xlsx format file is copied into the geo-detector software.
In some embodiments of the invention, a loess gully development influence mechanism analysis method based on a geographic detector sets the gully density in a data attribute table as a Y variable, sets the rest of data as an X variable, and performs calculation through geographic detection software to obtain and generate a risk detection result, a factor detection result, an ecological detection result and an interaction detection result.
The embodiment of the invention at least has the following advantages or beneficial effects:
the embodiment of the invention provides a loess gully development influence mechanism analysis method based on a geographic detector, the method comprises the steps of providing comprehensive data for subsequent analysis by acquiring a plurality of types of data such as DEM data, valley density data, first-class environmental data and second-class environmental data in the range of a loess plateau, preprocessing the acquired data, uniformly researching a projection coordinate system of the data, uniformly researching the grid size of the data, converting the acquired valley density grid data into a vector point data set, and simultaneously recording the valley density value at each point position, extracting point data subsets from the point data set according to a uniform distribution principle, dividing the point data subsets into 30 classes according to a natural discontinuous point classification method, reclassifying the second type of environment data according to a natural discontinuity point classification method, and adding the reclassified second type of environment data as attribute information into a point data subset attribute table; and exporting the data subset attribute information to geographical detector software, taking the valley density as a dependent variable and other environmental factors as independent variables, carrying out geographical detection calculation, finally obtaining various results such as risk detection, factor detection, ecological detection, interactive detection and the like, and carrying out more accurate and comprehensive analysis on the development of loess gullies.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart illustrating a loess gully development influence mechanism analysis method based on a geographical detector according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a method for extracting a point data subset in a loess ravine development influence mechanism analysis method based on a geographic detector according to an embodiment of the present invention;
fig. 3 is a loess plateau range diagram in a loess gully development influence mechanism analysis method based on a geographic detector according to an embodiment of the present invention;
fig. 4 is an erosion intensity level classification chart in a loess gully development influence mechanism analysis method based on a geographic detector according to an embodiment of the present invention;
fig. 5 is a classification diagram for classifying vegetation types in a loess gully development influence mechanism analysis method based on a geographic detector according to an embodiment of the present invention;
Fig. 6 is a classification chart of soil type classification in a loess gully development influence mechanism analysis method based on a geographical detector according to an embodiment of the present invention;
fig. 7 is a classification chart of land utilization type grade division in a loess gully development influence mechanism analysis method based on a geographical detector according to an embodiment of the present invention;
fig. 8 is a slope grade classification chart in a loess gully development influence mechanism analysis method based on a geographic detector according to an embodiment of the present invention;
fig. 9 is a classification diagram for grading the slope in a loess gully development influence mechanism analysis method based on a geographic detector according to an embodiment of the present invention;
fig. 10 is an NDVI grade classification chart in a loess gully development influence mechanism analysis method based on a geographical detector according to an embodiment of the present invention;
fig. 11 is a rainfall level classification chart in a loess gully development influence mechanism analysis method based on a geographic detector according to an embodiment of the present invention;
fig. 12 is a classification chart of yellow soil thickness classification in a loess gully development influence mechanism analysis method based on a geographical detector according to an embodiment of the present invention;
fig. 13 is a classification chart of sand content rating in a loess gully development influence mechanism analysis method based on a geographical detector according to an embodiment of the present invention;
Fig. 14 is a classification chart of the silt sand content grade division in the loess gully development influence mechanism analysis method based on the geographical detector according to the embodiment of the present invention;
fig. 15 is a classification chart of clay content classification in a loess gully development influence mechanism analysis method based on a geographic detector according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
In the description of the embodiments of the present invention, "a plurality" represents at least 2.
Examples
As shown in fig. 1 to 15, the present embodiment provides a loess gully development influence mechanism analysis method based on a geographic detector, including the following steps:
s1, acquiring DEM data, valley density data, first-class environmental data and second-class environmental data in the loess plateau range, and preprocessing the acquired DEM data, the valley density data, the first-class environmental data and the second-class environmental data to obtain raster data;
s2, converting the acquired valley density grid data into a vector point data set, simultaneously recording the valley density value at each point position, and extracting a point data subset from the vector point data set according to the uniform distribution principle;
s3, reserving raster data of first-class environment data in the raster data, and adding the first-class environment data serving as attribute information into an attribute table of the point data subset;
s4, reclassifying the second type of environment data in the raster data according to a natural breakpoint grading method;
s5, adding the reclassified second-class environment data as attribute information into the point data subset attribute table;
And S6, exporting the point data subset attribute information to geographic detector software for geographic detection calculation.
Analyzing a loess gully development influence mechanism, acquiring a plurality of types of data such as DEM data, gully density data, first type of environmental data and second type of environmental data in a loess plateau range, providing comprehensive data for subsequent analysis, preprocessing the acquired data, uniformly researching a projection coordinate system of the data, uniformly researching the grid size of the data, calculating and acquiring gradient data and slope direction data according to the DEM data, acquiring the grid data of the DEM data, the gully density data, the first type of environmental data and the second type of environmental data, converting the acquired gully density grid data into a vector point data set, simultaneously recording the gully density value of each point position, extracting a principle point data subset according to uniform distribution of the vector point data set, and classifying the first type of environmental data according to an original standard as shown in figures 4-7, the method comprises the steps of reserving original types of first-class environment data (land utilization data, erosion intensity data, soil type data and vegetation type data), namely grid data of the first-class environment data, adding the first-class environment data (the land utilization data, the erosion intensity data, the vegetation type data and the soil type data) at each point position as attribute information into an attribute table of a point data subset through a multi-value extraction to point tool in ArcGIS software, reclassifying the grid data (gradient data, slope direction data, NDVI data, rainfall data, loess thickness data and soil texture data) of second-class environment data in the grid data according to a natural breakpoint classification method, and classifying the second-class environment data into 30 types according to the natural breakpoint classification method; adding the reclassified second-class environment data serving as attribute information into a point data subset attribute table; and exporting the point data subset attribute information to geographic detector software, taking the valley density as a dependent variable and other environmental factors (land utilization, erosion intensity, soil type, vegetation type, gradient, slope direction, NDVI, rainfall, loess thickness and soil texture) as independent variables in the geographic detector software, performing geographic detection calculation, and finally obtaining risk detection, factor detection, ecological detection and interactive detection results. The method comprises the steps of analyzing various environmental factors influencing the development of the loess gully based on a geographic detector, realizing the dependency analysis of the loess gully development environment based on the geographic detector, establishing a nonlinear relation model between the loess gully development and the environmental factors, exploring the influence degrees of different environmental factors in the loess gully development process, finding an environmental factor influencing the loess gully development to the maximum extent, and carrying out more accurate and comprehensive analysis on the loess gully development.
In some embodiments of the present invention, a loess gully development influence mechanism analysis method based on a geographic detector includes the following steps of preprocessing acquired DEM data, gully density data, first type environmental data and second type environmental data to obtain grid data:
uniformly setting the projection coordinate system information of the acquired DEM data, the valley density data, the first type of environment data and the second type of environment data as a Mercator projection coordinate system, and acquiring initial raster data of the DEM data, the valley density data, the first type of environment data and the second type of environment data;
setting sampling grid size information;
and resampling the initial raster data of the acquired DEM data, the valley density data, the first type of environmental data and the second type of environmental data according to the set sampling raster size information to acquire target raster data.
Uniformly setting projection coordinate system information of research data such as DEM data, valley density data, land utilization data, erosion intensity data, vegetation type data, NDVI data in 2016 (year 2016) of 2000-; and uniformly resampling the obtained DEM data, valley density data, land utilization data, erosion intensity data, vegetation type data, NDVI data in 2016 (2000) year, rainfall data in 2015 (1980), loess thickness data, soil type data, soil texture data and other research data to 1km, and re-obtaining target grid data.
In some embodiments of the present invention, the method for preprocessing the acquired DEM data further comprises the steps of:
and analyzing the acquired DEM data, and calculating and acquiring gradient data and slope data according to the DEM data.
And calculating and acquiring gradient data and slope direction data according to the DEM data so as to perform comprehensive data analysis subsequently and ensure the comprehensiveness of the data analysis.
In some embodiments of the invention, the first type of environmental data comprises land use data, erosion intensity data, soil type data, and vegetation type data; the second type of environmental data includes grade data, slope data, NDVI data, rainfall data, loess thickness data, and soil texture data.
The method comprises the steps of obtaining first-class environmental data and second-class environmental data such as soil utilization data, erosion intensity data, soil type data and vegetation type data related to loess gully development, slope data, NDVI data, rainfall data, loess thickness data and soil texture data, providing comprehensive research data for follow-up analysis, and ensuring data research accuracy.
In some embodiments of the invention, the soil texture data comprises sand content data, silt content data and clay content data.
The soil texture data comprises loess plateau geological data such as sandy soil content data, silt content data and clay content data, factors influencing loess gully development are analyzed in detail, and the accuracy of data analysis is improved.
In some embodiments of the present invention, as shown in fig. 2, the method for extracting the point data subset from the point data set according to the uniform distribution principle includes the following steps:
a1, acquiring element information and element number N in the vector point data set;
a2, dividing the vector point data set into a first point data subset and a second point data subset, and distributing element information in the vector point data set into the first point data subset and the second point data subset according to a set proportion;
a3, acquiring the number L of elements in the first point data subset and the number N-L of elements in the second point data subset;
a4, generating a random value from the [0, 1] interval according to a uniform distribution principle, judging whether the random value is smaller than L/N, if so, distributing the pixel information to a first point data subset, and entering the step A5; if not, then assigning the pixel information to a second subset of point data, and proceeding to step A5;
a5, judging whether the element information in the vector point data set is distributed completely, if yes, outputting a first point data subset and a second point data subset; if not, step A4 is entered.
When one point (the information to be counted) is traversed, a random value between [0 and 1] is randomly generated in the program, the random value is compared with the value of L/N, if the random value is smaller than the value of L/N, the point (the information to be counted) is added into the first point data subset, otherwise, the point data subset is added into the second point data subset, the point data subsets extracted from the vector point data set are distributed uniformly, and over-concentration is avoided. After dividing the original vector point data set into a first point data subset and a second point data subset, both subsets are output in a vector form, i.e. a shp format file is used as an output result.
In some embodiments of the present invention, the method for reclassifying the second type of environment data according to the natural breakpoint hierarchy includes the following steps:
respectively importing the gradient data, the slope direction data, the NDVI data, the rainfall data, the loess thickness data and the soil texture data into a reclassification tool of ArcGIS software;
presetting classification categories according to a natural discontinuity point classification method, and reclassifying the imported data according to the classification categories; and marking the missing value generated in the re-classification process as NoData.
As shown in fig. 8 to 15, classification categories are preset as 30 according to a natural break point classification method, gradient data, slope direction data, NDVI data, rainfall data, loess thickness data, soil texture data (sand content data, silt content data, and clay content data), and the like are imported into the ArcGIS software according to the classification categories to perform reclassification, and the second-class environmental data are respectively classified into 30 levels to perform more detailed analysis.
In some embodiments of the present invention, the method of adding the reclassified second type of environment data as attribute information to the point data subset attribute table comprises the steps of:
and extracting the gradient data, the slope direction data, the NDVI data, the rainfall data, the loess thickness data and the soil texture data at each point position into a point data subset attribute table through a multi-value extraction to point tool in ArcGIS software.
In some embodiments of the invention, the method of exporting point data subset attribute information into geo-detector software comprises the steps of:
opening a point data subset attribute table from ArcGIS software, and exporting attribute table information into a dbf format file;
processing the data in the dbf format file, eliminating abnormal values to obtain a new data attribute table and storing the new data attribute table as an xlsx format file;
The data in the xlsx format file is copied into the geo-detector software.
Opening a dbf format file through Excel software or other software, eliminating abnormal values in the Excel software or other software, acquiring a new data attribute table and storing the new data attribute table as an xlsx format file; the data in the xlsx format file is copied into the geo-detector software.
In some embodiments of the present invention, a loess gully development influence mechanism analysis method based on a geographic detector sets a gully density as a Y variable and sets the rest of data as an X variable, calculates and generates a risk detection result, a factor detection result, an ecological detection result and an interaction detection result according to a formula, calculates by a geographic detector software, measures by a q value,
SST＝Nσ2(ii) a In the formula: h 1, …, L is a hierarchy (Strata) of variable Y or factor X, i.e. classification or partitioning; n is a radical ofhAnd N is divided intoThe number of units of layer h and the whole area;
and σ2The variance of the Y values for layer h and the whole region, respectively. SSW and SST are the Sum of intra-layer variance (Within Sum of Squares) and Total Sum of Total variance (Total Sum of Squares), respectively. q has a value range of [0, 1]Larger values indicate more pronounced spatial diversity of Y; if the hierarchy is generated by an argument X, a larger value of q indicates a stronger interpretation of the attribute Y by the argument X, and conversely, a weaker interpretation.
And (3) finding out the environmental factors which have large influence on the development of the loess ravines by analyzing the influence degree of each environmental factor on the development of the loess ravines. The factor of the geographic detector is q value, and the larger the q value is, the larger the influence of the independent variable on the dependent variable is, as can be seen from table 1: according to the q value, the influence of the environmental factors on the development of the loess gully is from large to small, namely the erosion strength is sand soil, silt soil, clay rainfall NDVI, soil type, loess thickness, gradient and land utilization type, vegetation type and slope direction, the erosion strength is most influenced in the development process of the loess gully, the loess gully accords with geomorphology cognition, the development of the gully in a loess plateau area is generally accompanied with severe ground surface erosion activity, the influence degree of soil texture factors such as the sand soil, the silt soil and the clay is also larger, the influence degree of the three on the development of the loess gully accords with subjective cognition, the further development of the loess gully cannot leave specific soil conditions, the water permeability of the sand soil is stronger, the silt soil is mainly the sand soil and the silt, the water permeability is poorer than the sand soil, but is stronger than the clay, and the soil mainly the silt is also easy to generate ground surface erosion, the clay is soil with small sand content and large viscosity, has poor water permeability and is not easy to generate water erosion. In the development process of loess gullies, rainfall is one of essential factors, so that rainfall has a great influence on the development of the gullies, the NDVI represents the coverage of surface vegetation, and the vegetation has a barrier effect on surface runoff, so that the NDVI also has an influence on the development process of the loess gullies, and different soil types have different soil properties and water retentivity, so that the soil types also have an influence on the development of the loess gullies. The influence of other environmental factors is less than 0.1, which indicates that the environmental factors have poor influence on single factors for loess ravine development.
Table 1:
the interactive detection result of the geographic detector shows that the development process of loess ravines is not the result of single factor action, but the result of multi-factor comprehensive action. As shown in table 2, the rainfall amount n loess thickness, erosion strength n sandy soil, erosion strength n silty soil and erosion strength n clay with q value over 0.4 indicate that the comprehensive effects of the above factors on the development of loess gullies are greater than the effects of the single factor, and further explain the promotion effects of the factors such as rainfall amount, erosion strength, loess thickness and soil texture in the development process of loess gullies.
Table 2:
in summary, embodiments of the present invention provide a loess gully development influence mechanism analysis method based on a geographic detector, which analyzes a loess gully development influence mechanism, obtains a plurality of types of data, such as DEM data, valley density data, first type of environmental data, second type of environmental data, and the like, in a loess plateau area, provides comprehensive data for subsequent analysis, preprocesses the obtained data, studies a projection coordinate system of the data in a unified manner, studies a grid size of the data in a unified manner, calculates and obtains gradient data and slope direction data according to the DEM data, obtains the grid data of the DEM data, the valley density data, the first type of environmental data, and the second type of environmental data, converts the obtained valley density grid data into a vector point data set, records a valley density value at each point position, extracts a point data subset from the point data set according to a uniform distribution principle, the method comprises the steps of reserving the original types of first-class environmental data (land utilization data, erosion intensity data and vegetation type data), reclassifying second-class environmental data (gradient data, slope direction data, NDVI (normalized difference vegetation index) data, rainfall data, loess thickness data, soil type data and soil texture data) according to a natural breakpoint classification method, and dividing the second-class environmental data into 30 classes according to the natural breakpoint classification method; adding the reclassified second-class environment data serving as attribute information into a point data subset attribute table; and exporting the attribute information of the point data subset to geographic detector software, taking the valley density as a dependent variable and other environmental factors as independent variables, and performing geographic detection calculation to finally obtain risk detection, factor detection, ecological detection and interaction detection results. The method comprises the steps of analyzing various environmental factors influencing the development of the loess gully based on a geographic detector, realizing the dependency analysis of the loess gully development environment based on the geographic detector, establishing a nonlinear relation model between the loess gully development and the environmental factors, exploring the influence degrees of different environmental factors in the loess gully development process, finding an environmental factor influencing the loess gully development to the maximum extent, and carrying out more accurate and comprehensive analysis on the loess gully development.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. A loess gully development influence mechanism analysis method based on a geographic detector is characterized by comprising the following steps:
the method comprises the steps of obtaining DEM data, valley density data, first type environmental data and second type environmental data in the loess plateau range, and preprocessing the obtained DEM data, the valley density data, the first type environmental data and the second type environmental data to obtain raster data;
Converting valley density raster data in the obtained raster data into a vector point data set, simultaneously recording the valley density value at each point position, and extracting a point data subset from the vector point data set according to an even distribution principle;
the method comprises the steps that raster data of first-class environment data in the raster data are reserved, and the first-class environment data are used as attribute information to be added into an attribute table of a point data subset;
reclassifying the second type of environment data in the raster data according to a natural discontinuity point classification method;
adding the reclassified second-class environment data serving as attribute information into an attribute table of the point data subset;
and exporting the attribute information in the attribute table of the point data subset to geographic detector software, performing geographic detection calculation, and acquiring a detection result.
2. The loess gully development influence mechanism analysis method based on the geographic detector as claimed in claim 1, wherein the method for preprocessing the acquired DEM data, the gully density data, the first type of environmental data and the second type of environmental data to obtain the grid data comprises the following steps:
uniformly setting the projection coordinate system information of the acquired DEM data, the valley density data, the first type of environment data and the second type of environment data as a Mercator projection coordinate system, and acquiring initial raster data of the DEM data, the valley density data, the first type of environment data and the second type of environment data;
Setting sampling grid size information;
and resampling the initial raster data of the acquired DEM data, the valley density data, the first type of environmental data and the second type of environmental data according to the set sampling raster size information to acquire target raster data.
3. The loess gully development influence mechanism analysis method based on the geographic detector as claimed in claim 2, wherein the method for preprocessing the acquired DEM data further comprises the following steps:
and analyzing the acquired DEM data, and calculating and acquiring gradient data and slope data according to the DEM data.
4. The loess ravine development influence mechanism analysis method based on a geographical detector as set forth in claim 1, wherein the first type environmental data comprises land utilization data, erosion intensity data, soil type data and vegetation type data; the second type of environmental data includes gradient data, slope data, NDVI data, rainfall data, loess thickness data and soil texture data.
5. The loess gully development influence mechanism analysis method as claimed in claim 4, wherein the soil texture data comprises sandy soil content data, silty soil content data and clay content data.
6. The loess ravine development influence mechanism analysis method based on a geographical detector as claimed in claim 1, wherein the method for extracting the point data subset from the vector point data set according to the uniform distribution principle comprises the following steps:
a1, acquiring element information and element number N in the vector point data set;
a2, dividing the vector point data set into a first point data subset and a second point data subset, and distributing element information in the vector point data set into the first point data subset and the second point data subset according to a set proportion;
a3, acquiring the number L of elements in the first point data subset and the number N-L of elements in the second point data subset;
a4, generating a random value from the [0, 1] interval according to a uniform distribution principle, judging whether the random value is smaller than L/N, if so, distributing the pixel information to a first point data subset, and entering the step A5; if not, then assigning the pixel information to a second subset of point data, and proceeding to step A5;
a5, judging whether the element information in the vector point data set is distributed completely, if yes, outputting a first point data subset and a second point data subset; if not, step A4 is entered.
7. The loess ravine development influence mechanism analysis method according to claim 4, wherein the method for reclassifying the second type of environmental data according to the natural breakpoint classification method comprises the following steps:
respectively importing the gradient data, the slope direction data, the NDVI data, the rainfall data, the loess thickness data and the soil texture data into a reclassification tool of ArcGIS software;
presetting classification categories according to a natural discontinuity point classification method, and reclassifying the imported data according to the classification categories; and marking the missing value generated in the re-classification process as NoData.
8. The loess ravine development influence mechanism analysis method based on a geographical detector as claimed in claim 4, wherein the method of adding the reclassified second-class environmental data as the attribute information to the attribute table of the point data subset comprises the steps of:
and extracting the gradient data, the slope direction data, the NDVI data, the rainfall data, the loess thickness data and the soil texture data at each point position into a point data subset attribute table through a multi-value extraction to point tool in ArcGIS software.
9. The loess ravine development influence mechanism analysis method based on a geographical detector as claimed in claim 1, wherein the method of exporting the point data subset attribute information to the geographical detector software comprises the following steps:
opening a point data subset attribute table from ArcGIS software, and exporting attribute table information into a dbf format file;
processing the data in the dbf format file, eliminating abnormal values to obtain a new data attribute table and storing the new data attribute table as an xlsx format file;
the data in the xlsx format file is copied into the geo-detector software.
10. The loess ravine development influence mechanism analysis method based on a geographical detector as claimed in claim 1, wherein the method for performing geographical detection calculation and obtaining detection result comprises the following steps:
and setting the valley density in the data attribute table as a Y variable and setting the rest data as an X variable, and calculating by using geographic detection software to obtain and generate a risk detection result, a factor detection result, an ecological detection result and an interaction detection result.
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