A method for judging spatial variation of soil indicators

Abstract

The application belongs to the technical field of soil exploration. A kind of judgment method of soil index space variation is disclosed, comprising the following steps: determining the soil index C for change detection;Using Kriging interpolation method to obtain regional data from the data C1 (x, y) of early observation point, to obtain the spatial data C1' (x, y) after interpolation and the spatial distribution data C1'E (x, y) of standard error;Using Kriging interpolation method to obtain regional data C2' (x, y) and the spatial distribution data C2'E (x, y) of standard error from the data C2 (x, y) of later observation point;Generate spatial data dC (x, y) to measure the change condition;Its standard error is calculated using the following formula to obtain the value of zC (x, y) : zC (x, y) = dC (x, y) / dCE (x, y) take the significance level α, the value of "invariable" interval is based on standard normal distribution function: [-Z (α / 2), +Z (α / 2)]. The method of the application can determine whether the soil index has "increased", "decreased" or not changed at a given significance level by using the constructed z value.

Description

Technical Field

[0001] This invention relates to the field of soil environmental detection technology, specifically to a method for judging the spatial variation of soil indicators. Background Technology

[0002] The description of the background art in this invention pertains to related technologies and is used merely for illustration and to facilitate understanding of the invention. It should not be construed as the applicant explicitly believing or presuming that the invention was prior art on the filing date of the first application.

[0003] Environmental indicators include basic soil properties such as pH and organic matter, and the content of nutrients such as nitrogen and phosphorus. They can also include the content of harmful elements such as cadmium, mercury, and arsenic, or healthy elements such as iodine and selenium; or they may be some chemically extracted form of the above elements. Regional soil environmental indicator changes are typically detected using methods such as soil geochemical surveys. Soil samples are collected and sent to laboratories for testing to obtain the spatial distribution of soil environmental indicators. However, current soil environmental management also needs to know the changes in these indicators: whether they are "increasing," "decreasing (or decreasing)," or "remaining unchanged," in order to answer questions such as whether the soil environmental situation is "continuing to deteriorate," "being contained," or "stabilizing and improving."

[0004] The above situation is typically assessed by revisiting data from two (or more) surveys or monitoring periods. However, this method faces several obstacles: observational data contains errors, data points cannot be perfectly aligned, and extrapolation from individual points to a broader area introduces uncertainty. This invention provides a data analysis solution to overcome these obstacles and arrive at scientific conclusions. Summary of the Invention

[0005] The purpose of this invention is to provide a method for judging the spatial variation of soil indicators. The method of this invention uses a constructed z-value to determine whether the soil indicators have "increased", "decreased" or remained unchanged at a given significance level.

[0006] The objective of this invention is achieved through the following technical solutions:

[0007] A method for determining the spatial variation of soil indicators includes the following steps:

[0008] Determine the soil index C for change detection;

[0009] The regional data C1(x,y) from the earlier observation point data C1(x,y) is obtained by using the Kriging interpolation method, resulting in the interpolated spatial data C1'(x,y) and the spatial distribution data C1'E(x,y) of its standard error; the regional data C2'(x,y) and the spatial distribution data C2'E(x,y) of its standard error are obtained by using the Kriging interpolation method from the later observation point data C2(x,y).

[0010] Generate spatial data dC(x,y) to measure changes; its standard error is:

[0011] The spatial data is obtained by calculating the value of zC(x,y) using the following formula:

[0012] zC(x,y)=dC(x,y) / dCE(x,y)

[0013] The significance level α is taken, and the values ​​of the "unchanged" interval are based on the standard normal distribution function: [-Z(α / 2), +Z(α / 2)].

[0014] Furthermore, the method for judging the spatial variation of soil indicators is characterized in that the method for generating spatial data dC(x,y) is to use the operation dC(x,y)=C2'(x,y)-C1'(x,y) to generate spatial data dC(x,y).

[0015] Furthermore, the method for judging the spatial variation of soil indicators is characterized in that the method for generating spatial data dC(x,y) is to use the operation d'(x,y)=C2'(x,y) / C1'(x,y) to generate spatial data dC(x,y).

[0016] Furthermore, the soil index C used for change detection is either a soil observation index or a transformed value of a soil observation index.

[0017] Furthermore, the transformed values ​​of the soil observation indicators are logarithmic transformations.

[0018] Furthermore, the Kriging interpolation methods include simple Kriging, ordinary Kriging, generalized Kriging, and regression Kriging.

[0019] Furthermore, it is required that the sampling points in each period be arranged relatively evenly and with sufficient density so that the selected interpolation method can objectively reflect the spatial distribution of index C.

[0020] The embodiments of the present invention have the following beneficial effects:

[0021] This invention provides the standard error of the change value zC(x,y). Using the constructed z value, it is possible to determine whether soil indicators have "increased", "decreased" or remained unchanged at a given significance level. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of existing detection methods;

[0023] Figure 2 This is a schematic diagram of the method of the present invention;

[0024] Figure 3 This is a comparison diagram of the effects of prior art and the present application in one embodiment of the present invention. Detailed Implementation

[0025] The present application will be further described below with reference to the embodiments.

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, in the following description, different "an embodiment" or "an embodiment" do not necessarily refer to the same embodiment. Different embodiments can be substituted or combined, and for those skilled in the art, other implementation methods can be obtained based on these embodiments without creative effort.

[0027] Combination Figure 1 The general operation of existing technologies is as follows:

[0028] This section describes the situation of the two phases (early and late phases) of the survey or monitoring. Data from multiple phases can be broken down into multiple "two-phase" scenarios.

[0029] (1) Data space interpolation

[0030] The spatial data C1'(x,y) is obtained by interpolating the earlier observation point data C1(x,y) using a certain interpolation method. This spatial data is usually expressed using a raster data model. The regional data C2'(x,y) is obtained by interpolating the later observation point data C2(x,y) using the same interpolation method, where (x,y) represents the spatial coordinates. The interpolation methods used include: inverse distance interpolation, Kriging interpolation, radial basis function, surface interpolation, Thiessen polygons, etc.

[0031] (2) Subtraction operation

[0032] Spatial data dC(x,y) is generated using the operation dC(x,y) = C2'(x,y) - C1'(x,y) to measure changes. When plotting dC(x,y), a threshold Ca is manually set. If the absolute value of the change dC(x,y) is within Ca, it is considered that there has been no change; otherwise, it is considered that there has been an increase (dC>0) or a decrease (dC<0).

[0033] Data transformation processes such as logarithmic transformation can be inserted before or after step (1) or (2).

[0034] This invention provides the standard error of the change value zC(x,y). Using the constructed z value, it is possible to determine whether the soil index has "increased", "decreased" or remained unchanged at a given significance level.

[0035] Combination Figure 2 The main steps of this invention include:

[0036] The spatialization of point observation data is performed using the Kriging spatial interpolation method, and the standard error of the spatial prediction is given.

[0037] The standard error of the calculation of the change:

[0038] Calculate the z-value:

[0039] The z-value is used to determine whether a significant change has occurred and to define the range of change.

[0040] This invention is achieved through the following scheme:

[0041] A method for determining the spatial variation of soil indicators includes the following steps:

[0042] Determine the soil index C for change detection;

[0043] The regional data C1(x,y) from the earlier observation point data C1(x,y) is obtained by using the Kriging interpolation method, resulting in the interpolated spatial data C1'(x,y) and the spatial distribution data C1'E(x,y) of its standard error; the regional data C2'(x,y) and the spatial distribution data C2'E(x,y) of its standard error are obtained by using the Kriging interpolation method from the later observation point data C2(x,y).

[0044] Generate spatial data dC(x,y) to measure changes; its standard error is:

[0045] The spatial data is obtained by calculating the value of zC(x,y) using the following formula:

[0046] zC(x,y)=dC(x,y) / dCE(x,y)

[0047] The significance level α is taken, and the values ​​of the "unchanged" interval are based on the standard normal distribution function: [-Z(α / 2), +Z(α / 2)].

[0048] In some embodiments, a significance level of α = 0.05 is used. If zC(x,y) is not within the interval [-1.96, 1.96], then the index C is considered to have changed significantly. The spatial data of zC(x,y) can be used to extract regions where zC(x,y) < -1.96, indicating a significant decrease (or reduction); regions where zC(x,y) > 1.96, indicating a significant increase; and regions where no significant change is detected. The significance level can also be set to α = 0.01, etc. For example, if α = 0.01, the corresponding interval is [-2.58, 2.58], meaning that the values ​​of α and the corresponding "unchanged" interval are based on the standard normal distribution function: [-Z(α / 2), +Z(α / 2)].

[0049] In some embodiments of the present invention, the method for judging the spatial variation of soil indicators is characterized in that the method for generating spatial data dC(x,y) is to use the operation dC(x,y)=C2'(x,y)-C1'(x,y) to generate spatial data dC(x,y).

[0050] In some embodiments of the present invention, the method for judging the spatial variation of soil indicators is characterized in that the method for generating spatial data dC(x,y) is to use the operation d'(x,y)=C2'(x,y) / C1'(x,y) to generate spatial data dC(x,y).

[0051] In some embodiments of the present invention, the soil index C used for change detection is a soil observation index or a transformed value of a soil observation index.

[0052] In some embodiments of the present invention, the transformed values ​​of the soil observation indicators are logarithmic transformations.

[0053] In some embodiments of the present invention, the kriging interpolation method includes simple kriging, ordinary kriging, generalized kriging, and regression kriging.

[0054] In some embodiments of the present invention, it is required that the sampling points in each period be arranged relatively uniformly and with sufficient density so that the selected interpolation method can objectively reflect the spatial distribution of index C.

[0055] The environmental indicators described in this invention include basic soil properties such as pH value and organic matter, and the content of nutrients such as nitrogen and phosphorus. They may also include the content of harmful elements such as cadmium, mercury, and arsenic, or the content of healthy elements such as iodine and selenium; or they may be some chemically extracted form of the above elements.

[0056] A comparison of the effects of specific embodiments and existing technologies is provided in [link to comparison]. Figure 3 :

[0057] In a certain study area, soil nitrogen (N) content data from a preliminary survey was obtained in 2004, with 12,320 observation points, denoted as N1(x,y). In 2014, 150 points were randomly selected for repeated observations, resulting in 150 later observation points, with the N content data denoted as N2(x,y). Ordinary kriging interpolation was performed on both datasets, yielding N1'(x,y) and N2'(x,y) respectively. Spatial data dN(x,y) was generated using the operation dN(x,y) = N2'(x,y) - N1'(x,y). In existing methods, a threshold of 300 mg / kg can only be set subjectively, meaning that areas with dN(x,y) > 300 are considered areas of increasing soil N content; areas with dN(x,y) < -300 are considered areas of decreasing soil N content; and the remaining areas, i.e., -300 ≤ dN(x,y) ≤ 300, are considered areas of unchanged soil N content. The results are shown in […]. Figure 3 The left image.

[0058] Using the method described in this invention, based on the standard error data N1'E(x,y) and N2'E(x,y) obtained by Kriging interpolation, the following calculations are performed:

[0059]

[0060] Next, calculate:

[0061] zN(x,y)=dN(x,y) / dNE(x,y)

[0062] Taking α = 0.05, we consider the region where zN(x,y) > 1.96 to be the region where soil N content increases; the region where zN(x,y) < -1.96 to be the region where soil N content decreases; and the remaining region, i.e., the region where -1.96 ≤ zN(x,y) ≤ 1.96, to be the region where soil N content remains unchanged. The results are shown in […]. Figure 3 The image on the right.

[0063] The results comparison shows that existing methods overestimate the areas where changes such as "increase" and "decrease" occur. This invention provides rigorous verification and threshold setting methods, avoiding subjectivity and providing relatively objective spatial variation analysis results.

[0064] It should be noted that the above embodiments can be freely combined as needed. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0065] It should be noted that the above embodiments can be freely combined as needed. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for determining the spatial variation of soil indicators, characterized in that, Includes the following steps: Determine the soil index C for change detection; The regional data C1(x,y) from the earlier observation point data C1(x,y) is obtained by using the Kriging interpolation method, resulting in the interpolated spatial data C1'(x,y) and the spatial distribution data of its standard error C1'E(x,y); the regional data C2'(x,y) from the later observation point data C2(x,y) is obtained by using the Kriging interpolation method, resulting in the spatial distribution data of its standard error C2'E(x,y). Generate spatial data dC(x,y) to measure changes; its standard error is: The spatial data is obtained by calculating the value of zC(x,y) using the following formula: The significance level is α, and the values ​​of the "unchanged" interval are based on the standard normal distribution function: [-Z(α / 2), +Z(α / 2)].

2. The method for determining the spatial variation of soil indicators according to claim 1, characterized in that, The method for judging the spatial variation of soil indicators is characterized in that the method for generating spatial data dC(x,y) is to use the operation dC(x,y)=C2'(x,y)-C1'(x,y) to generate spatial data dC(x,y).

3. The method for determining the spatial variation of soil indicators according to claim 1, characterized in that, The soil index C used for change detection is either a soil observation index or a transformed value of a soil observation index.

4. The method for determining the spatial variation of soil indicators according to claim 1, characterized in that, The Kriging interpolation methods mentioned include simple Kriging, ordinary Kriging, generalized Kriging, and regression Kriging.

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