CN112733310A - County soil attribute survey sampling point layout method based on composite type units - Google Patents
County soil attribute survey sampling point layout method based on composite type units Download PDFInfo
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Abstract
The invention provides a county soil attribute survey sampling point layout method based on composite type units, and belongs to the field of soil resource survey research. According to the method, a soil composite type landscape unit vector diagram is constructed in real time according to the granularity requirement of soil classification survey to obtain a characteristic landscape index value, the spatial variability of soil property to be surveyed in each composite type landscape unit and the number of survey sample points meeting the precision requirement are calculated, the soil survey sample points are randomly and uniformly distributed by utilizing the spatial distribution pattern of the composite type landscape units, the geographic and attribute spatial full coverage of the soil survey sample points is realized, the preset soil survey granularity and precision requirements are met, and the quality and efficiency of the soil survey are improved.
Description
One, the technical field
The invention relates to a sample point arrangement method for county soil attribute investigation through a spatial distribution pattern of a composite type soil landscape unit, in particular to a method for randomly and uniformly arranging soil investigation sample points according to the spatial distribution pattern of the soil composite type landscape unit, which comprises the steps of obtaining a characteristic landscape index value by constructing a composite type soil landscape unit vector distribution map with corresponding granularity according to the granularity and precision requirements of the soil investigation, predicting the spatial variability of soil investigation attribute items according to the characteristic landscape index value, further calculating the number of sample points of different soil attribute items meeting the precision requirements of the investigation, and finally randomly and uniformly arranging the soil investigation sample points according to the spatial distribution pattern of the soil composite type landscape unit, and belongs to.
Second, background Art
The scientific and reasonable design method of the soil sampling point survey scheme has important significance for improving the soil survey precision, the soil spatial information data quality and the working efficiency. The design of the sampling point survey scheme needs to solve three basic problems of sampling point representativeness, sampling point spatial position and sampling point density emphatically, wherein the spatial position and the distribution density of the soil sampling points are the most critical, the sampling points are required to be fully covered in regional geography and attribute space, the sampling point representativeness is ensured, and the overall characteristics of the regional soil are comprehensively reflected. The soil survey sampling point scheme design is summarized and summarized mainly in 2 modes, namely, traditional-based sampling strategies (DS) and model-based adaptive model design (MA) based on environment auxiliary variables.
Systematic random sampling or sequential random sampling (DS) is the simplest sampling Design (DS) method, and usually the sampling points are arranged in the survey area in a certain plane geometry. For example, in a polygonal mesh point arrangement method for covering a survey area, the center or intersection of a mesh is used as a sampling point, and the size of the mesh or the distance between sampling points is often determined according to the number of the sampling points artificially planned in advance. However, the artificially defined grid sampling area may include a plurality of soil and land utilization types, and the principle-free sampling design method without considering the main control factors can realize the full coverage of the geographic space, but the soil sampling points have poor representativeness, the attribute space is difficult to be fully covered, and the working efficiency is extremely low. Therefore, many applications utilize mathematical or geostatistical feature information of environmental auxiliary variables related to spatial variation of soil properties to perform sampling point design, such as auxiliary variables having significant influence or main control effect on soil properties, such as climate, hydrology, terrain, mother quality, land use, vegetation type, and the like.
Based on a traditional sampling Design (DS) mode of a single-factor auxiliary variable, such as a layered random sampling (structured random sampling), a whole group random sampling (Cluster) and other methods, layering, segmenting and grouping are carried out according to auxiliary variable statistical information, and the number of sampling points is determined according to the variability of auxiliary variables of each layer, segment or group; the greater the variability of the auxiliary variable, the greater the number of samples. A traditional sampling Design (DS) method based on multi-factor auxiliary variables, such as LHS (Latin Hypercube sampling), applies a Monte Carlo sampling scheme, carries out random layered sampling according to probability distribution of each auxiliary variable, but does not specify the actual sampling number in detail, only generally requires that the sampling number (u) is higher than the environment variable number (beta), and meets the requirement of the degree of freedom (n-u-beta) of a modeling sample. However, no matter based on single-factor or multi-factor environmental auxiliary variables, the DS sampling design mode considers the attribute space full coverage of the auxiliary variables, but weakens the geographic space full coverage, and the problem of the relation between the hierarchical sampling investigation granularity and the investigation precision based on the auxiliary variables and the distribution density of sampling points cannot be solved.
Based on an environment-aided variable Model design (MA) mode, firstly, a statistical Model (Geo-dynamics Model, GS) is used for analyzing the space variability of environment-aided variables, and parameters such as a semi-variable function (semi-variable) and a space autocorrelation variable (range) are used for determining the density or sampling interval of grid sampling points. The GS model sampling design method is limited to different degrees because the semi-variance function (semi-variance) of auxiliary variables needs to be mastered in advance. Another method, such as a square tree (VQT) model design (MA) method based on environment auxiliary variables, continuously quartering a target region by VQT technique until the variability of the auxiliary variables inside each partition meets a pre-designed threshold, and then taking each partition as a sampling area; and for a plurality of environment auxiliary variables, the main components of the environment auxiliary variables are used as partition auxiliary variables. VQT, the number of sampling points depends on the pre-designed internal auxiliary variable variability threshold of the partition, and the pre-formulated requirement of the number of sampling points is met by adjusting the threshold; VQT weakens the geographic space full coverage, and the spatial distribution of sampling points is extremely unbalanced, so that the spatial prediction accuracy of the soil property is low.
The DS and MA sampling point design method based on the environment auxiliary variable depends on the relation between soil investigation attribute and the auxiliary variable, but the relation is not enough to enable the spatial variability of the auxiliary variable to be equal to the spatial variability of the soil attribute to be investigated, obvious difference exists between the DS and MA sampling point design methods, and the deviation of the soil sampling point design scheme is inevitably caused according to the auxiliary variable information. Obviously, the principle of directly utilizing the spatial variation information of the soil attribute item to be investigated to carry out sampling point arrangement is the best method, but the spatial variation information of the soil attribute to be investigated is obtained, so that the method has no way for the area lacking prior information; even if the soil to-be-investigated attribute prior information is possessed, the prior information also has timeliness, the difference between the prior information and the reality is gradually increased along with the time lapse, the design scheme of the sampling points and the reality also have obvious deviation, the soil investigation precision requirement is difficult to be ensured, and the optimal sampling point arrangement method still has the same nominal form.
Therefore, aiming at the technical problems existing in the soil survey sampling point arrangement at present, the method aims to obtain the characteristic landscape index value by constructing the soil composite type landscape unit vector diagram, directly calculate and utilize the spatial variability of the soil property to be surveyed, carry out the soil survey sampling point arrangement based on the real-time soil composite type landscape unit distribution pattern, and can meet the requirements of the preset soil survey granularity and precision.
Third, the invention
The invention aims to provide a county soil attribute survey sampling point layout method based on composite type units. The principle of the invention is as follows:
soil is a comprehensive body of natural factors such as climate, geology, biology, hydrology and the like, and the attribute characteristics of the soil are influenced by artificial factors such as land utilization and the like. The soil properties of various types of units controlled by different influence factors are obviously different; the soil properties within the cells are also subject to variability, and differences between types of cells can also occur. The inter-type unit variability and intra-type variability are controlled by the type unit granularity. The type unit granularity refers to the size (length, area or volume) of the minimum identifiable landscape type unit patch, and can be characterized by a drawing scale and image resolution; the characterization, i.e., the classification granularity, can also be performed using an organizational classification hierarchy. The more control factors for dividing the type units, the smaller the granularity of each type unit, the stronger the difference between different types of units, and the stronger the representativeness of the survey sampling points distributed based on the type units; the smaller the internal variability of each type of unit, the less the quantity of the sample points for distribution survey, but the larger the number of the unit types, the more the total number of the sample points in the survey area will be increased. The number of regional soil survey sampling points is related to the granularity of the survey unit types, namely, the number of the survey unit types and the internal variability thereof.
The soil survey sampling point arrangement of a specific area depends on soil survey targets, survey unit granularity, survey precision requirements and soil space variability. The method comprises the steps of selecting corresponding tissue classification granularity according to the granularity requirement of a preset investigation unit, carrying out space superposition analysis on thematic element vector diagrams such as real-time land utilization types and soil types, and dividing a region to be investigated into different composite type landscape units according to two or more soil control factors; and selecting composite type landscape units according to the investigation target, and respectively taking the plaques of the various composite type landscape units as independent sample point distribution areas so as to ensure the representativeness of the investigation sample points and the full coverage of the soil attribute area. Judging the spatial variability of the soil attribute inside each type of unit by extracting the characteristic landscape index values of different composite type units; further calculating the number of sampling points of each composite type unit according to the soil survey precision requirement; and finally, according to the number of the sampling points, the sampling points are randomly and uniformly distributed in each independent sampling point distribution area respectively, so that the full coverage of the geographic space of the sampling points is realized.
The technical scheme of the invention mainly comprises the following steps:
(1) and acquiring vectorization spatial distribution data of each type of thematic element. And acquiring spatial distribution data of county domain thematic geographic elements such as land utilization, soil types, soil matrix types, administrative districts and the like by using digital means such as remote sensing and the like, and compiling a thematic element type vectorization distribution map. The minimum granularity of each thematic element classification needs to meet the granularity requirement of a soil investigation unit, and if the land utilization can adopt three-level classification of large class, medium class and small class according to national or industrial standards; the soil types can be classified according to the occurrence classification system according to soil types, subclasses, soil genera and soil species; administrative regions in county areas can be classified into villages, towns, villages and groups in three levels, and the like.
(2) And dividing the soil composite type landscape units. According to the soil survey target and the classification granularity requirement, vector graphic patches such as land utilization, soil types, soil matrix types and the like of corresponding classification granularity are selected and extracted, and a composite type unit vector distribution map (shown in figure 1) for distributing soil survey sampling points is established and compiled by utilizing a GIS space superposition technology. Such as investigation of soil organic matter content (attribute item P), land utilization selection first class classification (forest land a1, arable land a2, grassland A3, etc.) (fig. 1a), soil type selection soil type granularity classification (red soil B1, rice soil B2, moist soil B3, etc.) (fig. 1B), two factors are superposed to form a soil composite type unit (F), such as a forest land-red soil a1-B1, arable land-red soil a2-B1, arable land-rice soil a2-B2, arable land-moist soil a2-B3, grassland-moist soil A3-B3, etc. (fig. 1c), and research shows that the soil organic matter content has significant difference therebetween; the method is characterized in that the method not only has obvious representativeness for sample points distributed by various soil composite type units, but also covers an attribute numerical field of the organic matter content of the soil in the area.
(3) Computing characteristic landscape index value VAL of each soil composite type unitF L. Utilizing a composite type unit vector diagram laid by soil attribute survey sampling points, calculating a characteristic landscape index L with a remarkable spatial variability relation with a soil attribute item P by adopting Fragstats software at a grid resolution of 10 m multiplied by 10 m according to each landscape index description method of type level, and acquiring a characteristic landscape index value VAL corresponding to each composite type unit FF L。
(4) Calculating the spatial variation coefficient CV of the soil attribute items to be checked of each composite type unitF P. Aiming at each composite type unit under different classification granularity, the optimal quantitative relation model of the soil attribute item space variation coefficient CV and the corresponding characteristic landscape index L in each composite type unit F established by the method is respectively expressed by VALF LCalculating and acquiring the spatial variation coefficient CV of different soil attribute items P of each composite type unit F as an input itemF P。
(5) Calculating the number NUM of investigation sample points of different soil attribute items of each composite type unitF P. According to the confidence degree, the relative deviation and other precision requirements of soil attribute investigation, a sampling point formula is utilized
By CV ofF PCalculating the number NUM of survey sample points of the soil attribute item P of the composite type unit F meeting the precision requirement as an input itemF P. Where t is the t value for a certain degree of freedom (N-1) and significance level (. alpha.) and λ is the allowable relative deviation (%) between the proposed sampling survey and the population. Thus, the total number of sample points in the survey area for the attribute item P is
Where F is the sequence number of the unit type and n is the number of unit types.
(6) And (4) randomly and uniformly distributing the county soil survey sampling point space. Aiming at a certain soil attribute item P to be investigated separately, and counting NUM (number of soil investigation sample points) by each composite type unit FF PThe basis is; aiming at the simultaneous investigation of a plurality of different soil attribute items, the number NUM of sampling points with the most investigation requirements in each composite type unit soil attribute itemF PAccording to the method, soil investigation sampling points (shown in figure 2) are uniformly distributed in space by utilizing a GIS technology in each composite type unit F plaque distribution area, and the requirement of full coverage of the sampling point geographic space is met.
(7) And integrating the survey sampling points of each composite type unit by utilizing the GIS technology, and compiling a county soil attribute survey sampling point distribution diagram, sampling point space geographic coordinates and a soil composite type attribute table to finish the distribution of the sampling points.
Description of the drawings
FIG. 1 is a schematic view of a soil composite type landscape unit
(a) Land use type unit vectorized profile
(b) Soil type unit vectorized profile
(c) Composite type unit quantitative loss distribution diagram of land utilization and soil type
FIG. 2 is a schematic diagram of random and uniform arrangement of soil survey sampling points based on spatial distribution of composite type units
Fifth, detailed description of the invention
The invention is further described below by way of specific examples:
based on the precision requirements of investigation granularity, confidence degree (95%) and relative difference (5%) of a composite type landscape unit of land utilization (primary class C1) and soil type (subclass G2), the implementation method and the steps of investigating sample point layout aiming at the attributes of soil organic matters and the like of county-area cultivated land by utilizing a composite type landscape unit distribution pattern:
(1) and acquiring a unit vectorization space distribution map of the land utilization type and the soil type of the Yujiang county.
(2) And extracting a land utilization large class (C1) and a soil type sub-class (G2) vector pattern spot for spatial superposition by utilizing a GIS spatial analysis technology, and establishing and compiling a composite type unit vector partition map of cultivated land and the soil sub-class.
(3) Fragstats software was used to calculate the characteristic landscape index values of soil composite type units in Yunjiang county arable land at 10 m × 10 m grid resolution (Table 1).
(4) And (3) solving the spatial variation coefficient CV (table 3) of each soil attribute to be checked of the farmland soil compound type units in Yujiang county by utilizing the characteristic landscape index values (table 1) of the farmland soil compound type units and through a calculation model (table 2) of corresponding classification granularity.
(5) Based on the accuracy requirement that the confidence coefficient is 95% and the relative difference is 5%, the number of sample points of the soil composite type unit of each cultivated land in Yujiang county is calculated (table 4). The calculation method is the same as the step (5).
(6) And respectively and uniformly distributing soil survey sampling points at random according to the calculated sampling point number and based on the spatial distribution of the soil compound type unit of each farmland, and compiling a county-area farmland soil survey sampling point distribution diagram and an attribute table.
TABLE 1 soil composite type Unit characteristic landscape index value in Yujiang county
*: shape index: CONTIG _ MN, neighbor index _ mean; CIRCLE _ AM, area weighted average of the relevant circumcircle; PARA _ MN, perimeter area ratio _ average. **: independent/adjacent indices: ENN _ MN, geometric nearest neighbor distance _ mean.
Table 2 calculation model for obtaining soil property spatial variation coefficient (y) by using soil subclass (G2 classification granularity) composite type unit characteristic landscape index (x)
*: shape index: CONTIG _ MN, neighbor index _ mean; CIRCLE _ AM, area weighted average of the relevant circumcircle; PARA _ MN, perimeter area ratio _ average. **: independent/adjacent indices: ENN _ MN, geometric nearest neighbor distance _ mean.
TABLE 3 soil composite type Unit soil Attribute spatial variation coefficient of cultivated land in Yujiang county
TABLE 4 soil Compound type Unit soil Property survey sample Point number in Yujiang county cultivated land
Claims (2)
1. A county soil attribute survey sampling point layout method based on composite type units mainly comprises the following steps:
(1) obtaining vectorization spatial distribution data of various types of thematic elements: acquiring county domain thematic geographic element spatial distribution data such as land utilization, soil types and the like by using digital means such as remote sensing and the like, and compiling vectorized type distribution maps of the thematic elements;
(2) dividing a composite type soil landscape unit area: selecting and extracting special subject element types and other vector graphic patches with corresponding classification granularity according to the soil survey target and the granularity requirement, and establishing and compiling a composite type unit vector partition map of a survey area by utilizing a vector graphic patch GIS space superposition technology;
(3) computing characteristic landscape index value VAL of each soil composite type unitF L: utilizing the vector diagram of the composite type units, and aiming at the type landscape index L which is obviously related to the variation of the soil property, calculating and obtaining the corresponding characteristic landscape index value VAL of each composite type unit F through Fragstats softwareF L；
(4) Calculating the spatial variation coefficient CV of each composite type unit soil attributeF P: the composite type unit soil attribute space variation coefficient CV established by the method and the corresponding type landscape index value VAL quantitative relation model are used as VALF LCalculating and acquiring the spatial variation coefficient CV of the soil attribute item P of the composite type unit F as an input itemF P；
(5) Calculating the number NUM of investigation sample points of different soil attribute items of each composite type unitF P: according to the precision requirements of soil property investigation confidence coefficient, relative deviation and the like, a sampling point formula is utilized
By CV ofF PCalculating the investigation sampling point NUM of the soil attribute item P of the composite type unit F meeting the precision requirement as an input itemF P；
(6) Spatial arrangement of county soil survey sampling points: investigating number NUM of sample points by using soil attribute item P of each composite type unit FF PBased on the patch distribution of each composite type unit F, randomly and uniformly distributing soil survey sampling points in space by utilizing a GIS technology;
(7) and integrating the survey sampling points of the composite type units by utilizing the GIS technology, and compiling a county soil quality survey sampling point distribution diagram and a sampling point type attribute table to complete a sampling point layout task.
2. The county soil attribute survey sampling point arrangement method based on the composite type units as claimed in claim 1, wherein in the steps (2), (3), (4), (5) and (6), the characteristic landscape index value of each composite type unit is obtained through calculation by constructing a soil composite type unit vector diagram, the spatial variation coefficient of different soil survey attribute items of each composite type unit is calculated, the number of survey sampling points of different soil attribute items of each composite type unit is further calculated, and finally, the soil survey sampling points are uniformly arranged at random according to the space distribution of the patches of each composite type unit, so that the preset soil survey granularity and precision requirements are met.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114971234A (en) * | 2022-05-11 | 2022-08-30 | 北京中色测绘院有限公司 | Novel technical method for soil general survey |
| CN115310719A (en) * | 2022-09-16 | 2022-11-08 | 中国科学院地理科学与资源研究所 | Farmland soil sampling scheme design method based on three-stage k-means |
| CN117419955A (en) * | 2023-12-18 | 2024-01-19 | 中国农业科学院农业资源与农业区划研究所 | Soil on-site investigation sampling device and method based on electronic fence |
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2021
- 2021-01-28 CN CN202110138787.8A patent/CN112733310A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114971234A (en) * | 2022-05-11 | 2022-08-30 | 北京中色测绘院有限公司 | Novel technical method for soil general survey |
| CN114971234B (en) * | 2022-05-11 | 2023-04-07 | 北京中色测绘院有限公司 | Soil general survey technical method |
| CN115310719A (en) * | 2022-09-16 | 2022-11-08 | 中国科学院地理科学与资源研究所 | Farmland soil sampling scheme design method based on three-stage k-means |
| CN117419955A (en) * | 2023-12-18 | 2024-01-19 | 中国农业科学院农业资源与农业区划研究所 | Soil on-site investigation sampling device and method based on electronic fence |
| CN117419955B (en) * | 2023-12-18 | 2024-03-26 | 中国农业科学院农业资源与农业区划研究所 | Soil on-site investigation sampling device and method based on electronic fence |
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