A regional soil respiration monitoring method and system based on GIS technology

Abstract

The present invention discloses a regional soil respiration monitoring method and system based on GIS technology, which performs grid division on the monitoring area, obtains the spatial data set of the monitoring area, performs spatial autocorrelation analysis, calculates the global Moran index I value, Z score, p value, identifies the spatial clustering of elements with high or low values, and the spatial mutation value; determines the point set that meets the actual measurement requirements, and measures the soil respiration data of these monitoring points in the field, performs spatial interpolation according to the measured soil respiration data, until the soil respiration of the entire monitoring area is obtained; the system uses a computer to execute the above steps. The present invention not only effectively optimizes and reduces the number of monitoring points, realizes the monitoring and estimation of soil respiration in large-scale and large-range areas, but also can significantly improve the work efficiency of ecological environment monitoring. Compared with the existing mathematical model method, the monitoring results of the present invention are more real and accurate, the regional applicability is also higher, and the versatility is better.

Description

Technical Field

[0001] The present invention relates to the technical field of soil respiration monitoring, and in particular to a regional soil respiration monitoring method and system based on GIS technology. Background Art

[0002] Soil respiration is an important process in the carbon cycle of soil ecosystems. It is the result of the balance of soil carbon assimilation and dissimilation and an important source of atmospheric CO2, accounting for about 50%-80% of the respiration of the entire ecosystem. It is mainly produced by the respiration of soil animals, microorganisms and roots. Accurately measuring its release is the key to evaluating biological processes in ecosystems. Usually, fixed sample plots are set up in the monitoring area and measured using the box method (which can be divided into static method and dynamic method). However, the results of this method can only represent the individual situation of a single point and cannot reflect the overall level of the region.

[0003] At present, the method for determining the soil respiration of regional ecosystems mainly relies on estimation: based on the ground measured values, a mathematical fitting model of soil respiration and environmental factors such as temperature and precipitation is obtained, and then the spatial data set of temperature and precipitation is used to invert the soil respiration. For example, the method for estimating the total carbon release of soil respiration provided in document CN105588932B (select a test area, measure the soil respiration rate in the test area at different time points in the reference time period, and calculate the total carbon release of soil respiration in the time period corresponding to each time point; fit the time period corresponding to each time point and the soil respiration rate to obtain the fitting curve equation 1; fit the soil respiration rate and the total carbon release of soil respiration to obtain the fitting curve equation 2; use the equation to calculate the total carbon release of soil respiration under the same conditions as when the model was constructed). However, the disadvantage of this estimation method is that the mathematical fitting accuracy is insufficient, and the fitting model is difficult to be universal in different regions, making it difficult to promote and apply. Summary of the invention

[0004] At least in order to solve the technical problems mentioned in the background technology, the present invention provides a regional soil respiration monitoring method and system based on GIS technology, and the specific technical solution is as follows.

[0005] A regional soil respiration monitoring method based on GIS technology, comprising the following steps:

[0006] Step 1: determine the monitoring area and divide the monitoring area into grids, with each grid corresponding to a monitoring point;

[0007] Step 2, obtain the spatial data set of the monitoring area, including the spatial distribution data of six indicators: surface temperature (X1), precipitation (X2), altitude (X3), soil type (X4), vegetation type (X5), and ecosystem type (X6), and extract the attribute values ​​of the spatial data set of each monitoring point;

[0008] Step 3: Perform spatial autocorrelation analysis on the attribute values ​​of each monitoring point, calculate the global Moran index I value, Z score, and p value, and identify the spatial clustering of elements with high or low values ​​and spatial mutation values ​​through I value, Z score, and p value;

[0009] Step 4: According to the calculated results, each monitoring point is optimized to obtain a point set A={A1, A2, ..., A n}, n represents the nth monitoring point;

[0010] Step 5, based on the point set A, the soil respiration data of these monitoring points are measured on the spot;

[0011] Step 6: Perform spatial interpolation based on the measured soil respiration data to obtain the predicted values ​​of other monitoring points until the soil respiration of the entire monitoring area is obtained.

[0012] As a preferred solution, in step 3, the monitoring points are divided into type 1 NS, type 2 HH, type 3 HL, type 4 LH, and type 5 LL.

[0013] in,

[0014] Type 1 NS is defined as p value > 0.05,

[0015] Type II HH is defined as p value ≤ 0.05, I value > 0, and Z score > 0.

[0016] Type III HL is defined as p value ≤ 0.05, I value < 0, and Z score > 0.

[0017] Type IV LH was defined as p value ≤ 0.05, I value > 0, and Z score < 0.

[0018] Type five LL was defined as p value ≤ 0.05, I value < 0, and Z score < 0.

[0019] As a preferred solution, in step 4, the number of times type three HL and type four LH appear in the calculation results of the six indicators for each monitoring point is counted. If the number of type three HL or type four LH is less than 1, indicating that the six indicators of the monitoring point and its adjacent monitoring points have the same cluster and high correlation, it will be deleted; otherwise, the point will be retained.

[0020] As a preferred solution, the spatial resolution of the grid is not less than 250m×250m.

[0021] A regional soil respiration monitoring system based on GIS technology includes a computer control system, the computer control system includes a memory, a processor, and a program stored in the memory and executable on the processor, and the processor implements the following steps / functions when executing the program:

[0022] Step S1, read the selected monitoring area, divide the monitoring area into grids, and each grid corresponds to a monitoring point;

[0023] Step S2, obtaining the spatial data set of the monitoring area, including the spatial distribution data of six indicators: surface temperature (X1), precipitation (X2), altitude (X3), soil type (X4), vegetation type (X5), and ecosystem type (X6), and extracting the attribute values ​​of the spatial data set of each monitoring point;

[0024] Step S3, performing spatial autocorrelation analysis on the attribute values ​​of each monitoring point, calculating the global Moran's index I value, Z score, and p value, and identifying the spatial clustering of elements with high or low values ​​and spatial mutation values ​​through the I value, Z score, and p value;

[0025] The monitoring points are divided into type 1 NS, type 2 HH, type 3 HL, type 4 LH, and type 5 LL, among which type 1 NS is defined as p value > 0.05, type 2 HH is defined as p value ≤ 0.05, I value > 0, Z score > 0, type 3 HL is defined as p value ≤ 0.05, I value < 0, Z score > 0, type 4 LH is defined as p value ≤ 0.05, I value > 0, Z score < 0, and type 5 LL is defined as p value ≤ 0.05, I value < 0, and Z score < 0;

[0026] Step S4: according to the obtained calculation results, the number of times that type 3 HL and type 4 LH appear in the calculation results of the six indicators for each monitoring point is counted. If the number of type 3 HL or type 4 LH is less than 1, it indicates that the six indicators of the monitoring point and its adjacent monitoring points have the same cluster and high correlation, and the monitoring point is deleted; otherwise, the point is retained to obtain a point set A that meets the actual measurement requirements = {A1, A2, ..., A n};

[0027] Step S5, reading the soil respiration data measured in the field corresponding to the obtained point set;

[0028] Step S6, performing spatial interpolation according to the soil respiration data in step S5 to obtain predicted values ​​of other monitoring points until the soil respiration of the entire monitoring area is obtained;

[0029] Step S7, output monitoring results.

[0030] Beneficial effects: The present invention not only effectively optimizes and reduces the number of monitoring points, realizes soil respiration monitoring and estimation in large-scale and large-range areas, but also can significantly improve the efficiency of ecological environment monitoring. Compared with the existing mathematical model method, the monitoring results of the present invention are more real and accurate, with higher regional applicability and better versatility. The present invention can ensure that the monitoring points are fully covered, which is not only convenient for judging the correlation of indicator data between different points, but also takes into account the spatial distance relationship between different points, which is obviously different from the simple calculation of indicator data correlation mentioned in other monitoring point deployment methods. The scheme of the present invention is less affected by geological factors and is almost not affected by the high spatial heterogeneity of regional soil and complex geographical conditions. The accuracy and representativeness of the measured values ​​can be guaranteed without deploying a large number of monitoring points. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a schematic diagram of the grid division of the Tongluo Mountain monitoring area in Example 1; Figure 2 It is the spatial dataset of the Tongluo Mountain monitoring area in Example 1; Figure 3 It is the original data map of the monitoring points in the Tongluo Mountain monitoring area in Example 1; Figure 4 The global Moran index calculation result of the Tongluo Mountain monitoring area in Example 1; Figure 5 This is a schematic diagram of the point set that meets the actual measurement requirements in the Tongluo Mountain monitoring area in Example 1. DETAILED DESCRIPTION

[0032] The technical solutions of the present invention are described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments.

[0033] Example 1: A regional soil respiration monitoring method based on GIS technology is described by taking the soil respiration monitoring of the ecosystem of Tongluoshan Mine Park in Chongqing as an example. The steps are as follows:

[0034] Step 1: Determine the monitoring area (Tongluoshan Mine Park, Chongqing) and divide the monitoring area into grids, with each grid corresponding to a monitoring point. Specifically, in ArcGIS software, import the monitoring area boundary line of Tongluoshan Mine Park and use the "Create Fishnet" tool to form a 250m×250m grid in the monitoring area as the base map of the monitoring points, forming a total of 401 points, as shown in the figure. Figure 1 As shown;

[0035] Step 2, obtain the spatial data set of the monitoring area, including the spatial distribution data of surface temperature (X1), precipitation (X2), altitude (X3), soil type (X4), vegetation type (X5), and ecosystem type (X6), and extract the attribute values ​​of the spatial data set of each monitoring point;

[0036] The spatial dataset and raw data of Tongluoshan Mine Park are obtained as follows: Figure 2 and Figure 3 As shown, its altitude ranges from 141 to 1027m, soil types are divided into unsaturated soil and simple highly active strong acid soil, the surface temperature adopts the annual average temperature of the region in 2022, the precipitation adopts the annual average precipitation of the region in 2022, the vegetation types are divided into evergreen coniferous forest, shrub and coniferous and broad-leaved mixed forest, and the ecosystem types are divided into forest ecosystem, farmland ecosystem, wetland ecosystem, urban ecosystem shrub ecosystem and other ecosystems;

[0037] Step 3: Perform spatial autocorrelation analysis on the attribute values ​​of each monitoring point, calculate the global Moran's index I value, Z score, and p value, and identify the spatial clustering of elements with high or low values ​​and spatial mutation values ​​through Z score and p value; divide the monitoring points into type 1 NS, type 2 HH, type 3 HL, type 4 LH, and type 5 LL, among which type 1 NS is defined as p value > 0.05, type 2 HH is defined as p value ≤ 0.05, I value > 0, Z score > 0, type 3 HL is defined as p value ≤ 0.05, I value < 0, Z score > 0, type 4 LH is defined as p value ≤ 0.05, I value > 0, Z score < 0, type 5 LL is defined as p value ≤ 0.05, I value < 0, Z score < 0;

[0038] Based on the spatial data set and its original data obtained above, the results of calculating the global Moran index I value, Z score, and p value are shown in the table below. Figure 4 As shown;

[0039] In this step, the global Moran index is calculated as follows:

[0040]

[0041] In the formula, I i represents the Moran index at the i-th point, x i 、x j represents the attribute value of a certain indicator at the i-th and j-th points respectively (such as rainfall is 1150 mm, and the ecosystem type is a forest ecosystem), X represents the average value of a certain indicator attribute value, S i 2 Represents the square of the standard deviation of a certain indicator attribute value, w i,j is the spatial weight between the i-th point and the j-th point, and n represents the total number of initial points. And:

[0042]

[0043] n represents the total number of initial points;

[0044] The calculation method of the Z score of the ith point of the statistical data is as follows:

[0045]

[0046] in,

[0047]

[0048] V[I i ]=E[I i 2 ]-E[I i ] 2

[0049] Note: Not Significant (NS) means no significance; Moran's index is positive and Z score is positive, that is, High-High Cluster (HH), indicating high-high clustering, the Xn of this point is high, and the Xn of the adjacent area is also high; Moran's index is negative and Z score is positive, that is, High-Low Outlier (HL), indicating high-low clustering, the Xn is high, and the Xn of the adjacent area is low; Moran's index is positive and Z score is negative, that is, Low-High Outlier (LH), indicating low-high clustering, the Xn of this point is low, and the Xn of the adjacent area is high; Moran's index is negative and Z score is negative, that is, Low-Low Cluster (LL), indicating low-low clustering, the Xn of this point is low, and the Xn of the adjacent area is also low.

[0050] The calculation results in this embodiment are as follows:

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062] Step 4: According to the calculated results, each monitoring point is optimized to obtain a point set A={A1, A2, ..., A n}, n represents the nth monitoring point;

[0063] The number of times that type 3 HL and type 4 LH appear in the calculation results of the six indicator data for each monitoring point is counted. If the number of type 3 HL or type 4 LH is less than 1, it indicates that the six indicators of the monitoring point and its adjacent monitoring points have the same cluster and high correlation, and it will be deleted; otherwise, the point will be retained (that is, when the number of occurrences of type 3 LH or type 4 HL is ≥1, the point will be retained). In this example, 64 points {A1, A2, …, A 64},like Figure 5 As shown;

[0064] Step 5, based on the point set A, the soil respiration data of these monitoring points are measured on the spot;

[0065] Specifically, the coordinates of the point set A were used to locate the Tongluoshan Mine Park, and field measurements were carried out at the monitoring points using a high-precision CH4 / CO2 / H2O analyzer (LI-7810, LICOR, USA) to obtain the soil CO2 flux value, which is the soil respiration Rs;

[0066] Step 6: Perform spatial interpolation based on the measured soil respiration data to obtain the predicted values ​​of other monitoring points until the soil respiration of the entire monitoring area is obtained. Specifically, in ArcGIS software, the measured soil respiration Rs data with coordinates are spread out, and then Kriging interpolation is performed to obtain the predicted values ​​of other monitoring points, thereby obtaining the soil respiration in the entire monitoring area.

[0067] Result verification: Five monitoring points that are not in point set A were randomly selected, and actual measurement verification was carried out on these five monitoring points. The results are shown in Table 2.

[0068] Table 2 Comparison of predicted values ​​and actual values ​​at 5 monitoring points

[0069] Serial number Measured value of soil respiration Predicted soil respiration Monitoring point A 2.43943 2.63889 Monitoring point B 4.0587 3.55505 Monitoring point C 4.81988 4.34264 Monitoring point D 5.49486 4.77220 Monitoring point E 4.73265 4.00943

[0070] As shown in Table 2, the deviation between the predicted values ​​and the actual values ​​of the five monitoring points is within 20%, which meets the requirements of ecological measurement and estimation.

[0071] Embodiment 2: This embodiment provides a regional soil respiration monitoring system based on GIS technology, which implements soil respiration monitoring with the help of a computer system. The system includes a computer control system, which includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps / functions are implemented:

[0072] Step S1, read the selected monitoring area, divide the monitoring area into grids, and each grid corresponds to a monitoring point;

[0073] Step S2, obtaining the spatial data set of the monitoring area, including the spatial distribution data of surface temperature (X1), precipitation (X2), altitude (X3), soil type (X4), vegetation type (X5), and ecosystem type (X6), and extracting the attribute values ​​of the spatial data set of each monitoring point;

[0074] Step S3, performing spatial autocorrelation analysis on the attribute values ​​of each monitoring point, calculating the global Moran's index I value, Z score, and p value, and identifying the spatial clustering of elements with high or low values ​​and spatial mutation values ​​through the Z score and p value;

[0075] The monitoring points are divided into type 1 NS, type 2 HH, type 3 HL, type 4 LH, and type 5 LL, among which type 1 NS is defined as p value > 0.05, type 2 HH is defined as p value ≤ 0.05, I value > 0, Z score > 0, type 3 HL is defined as p value ≤ 0.05, I value < 0, Z score > 0, type 4 LH is defined as p value ≤ 0.05, I value > 0, Z score < 0, and type 5 LL is defined as p value ≤ 0.05, I value < 0, and Z score < 0;

[0076] Step S4: according to the calculation results, the number of times type 3 HL and type 4 LH appear at each monitoring point is counted. If the number of type 3 HL or type 4 LH is less than 1, it indicates that the 6 indicators of the monitoring point and its adjacent monitoring points have the same clustering and high correlation, and the monitoring point is deleted; otherwise, the point is retained to obtain a point set A that meets the actual measurement requirements = {A1, A2, ..., A n};

[0077] Step S5, reading the soil respiration data measured in the field corresponding to the obtained point set;

[0078] Step S6, performing spatial interpolation according to the soil respiration data in step S5 to obtain predicted values ​​of other monitoring points until the soil respiration of the entire monitoring area is obtained;

[0079] Step S7, output monitoring results.

[0080] The present invention not only effectively optimizes and reduces the number of monitoring points, realizes soil respiration monitoring and estimation in large-scale and large-range areas, but also can significantly improve the efficiency of ecological environment monitoring. Compared with the existing mathematical model method, the monitoring results of the present invention are more real and accurate, and the regional applicability is higher and the versatility is better. The scheme of the present invention can ensure that the monitoring points are fully covered, which is not only convenient for judging the correlation of indicator data between different points, but also takes into account the spatial distance relationship between different points, which is obviously different from the simple calculation of indicator data correlation mentioned in other monitoring point deployment methods. The scheme of the present invention is less affected by geological factors and is almost not affected by the high spatial heterogeneity of regional soil and complex geographical conditions. The accuracy and representativeness of the measured values ​​can be guaranteed without deploying a large number of monitoring points.

Claims

1. A regional soil respiration monitoring method based on GIS technology, characterized in that the steps include: Step 1: determine the monitoring area and divide the monitoring area into grids, with each grid corresponding to a monitoring point; Step 2: Obtain the spatial data set of the monitoring area, including the spatial distribution data of six indicators: surface temperature (X1), precipitation (X2), altitude (X3), soil type (X4), vegetation type (X5), and ecosystem type (X6), and extract the attribute values ​​of the spatial data set of each monitoring point; Step 3: Perform spatial autocorrelation analysis on the attribute values ​​of each monitoring point, calculate the global Moran index I value, Z score, and p value, and identify the spatial clustering of elements with high or low values ​​and spatial mutation values ​​through I value, Z score, and p value; The monitoring points are divided into type 1 NS, type 2 HH, type 3 HL, type 4 LH, and type 5 LL, among which: Type 1 NS was defined as p value > 0.05, Type II HH is defined as p value ≤ 0.05, I value > 0, and Z score > 0. Type III HL was defined as p value ≤ 0.05, I value < 0, and Z score > 0. Type IV LH was defined as p value ≤ 0.05, I value > 0, and Z score < 0. Type five LL was defined as p value ≤ 0 .05, I value < 0, and Z score < 0; Step 4: According to the calculated results, each monitoring point is optimized to obtain a point set A={A1, A2, ..., An} that meets the actual measurement requirements, where n represents the nth monitoring point; The number of times type 3 HL and type 4 LH appear in the calculation results of the six indicators for each monitoring point is counted. If the number of type 3 HL or type 4 LH is less than 1, it indicates that the six indicators of the monitoring point and its adjacent monitoring points have the same cluster and high correlation, and the monitoring point will be deleted; otherwise, the point will be retained; Step 5, based on the point set A, the soil respiration data of these monitoring points are measured on the spot; Step 6: Perform spatial interpolation based on the measured soil respiration data to obtain the predicted values ​​of other monitoring points until the soil respiration of the entire monitoring area is obtained.

2. The regional soil respiration monitoring method according to claim 1, characterized in that: The spatial resolution of the grid shall not be less than 250m×250m.

3. A regional soil respiration monitoring system based on GIS technology, comprising a computer control system, the computer control system comprising a memory, a processor and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the following steps / functions are implemented: Step S1, read the selected monitoring area, divide the monitoring area into grids, and each grid corresponds to a monitoring point; Step S2, obtaining the spatial data set of the monitoring area, including the spatial distribution data of six indicators: surface temperature (X1), precipitation (X2), altitude (X3), soil type (X4), vegetation type (X5), and ecosystem type (X6), and extracting the attribute values ​​of the spatial data set of each monitoring point; Step S3, perform spatial autocorrelation analysis on the attribute values ​​of each monitoring point, calculate the global Moran index I value, Z score, and p value, and identify the spatial clustering of elements with high or low values ​​and spatial mutation values ​​through I value, Z score, and p value; the monitoring points are divided into type 1 NS, type 2 HH, type 3 HL, type 4 LH, and type 5 LL, where: Type 1 NS was defined as p value > 0.05, Type II HH is defined as p value ≤ 0.05, I value > 0, and Z score > 0. Type III HL was defined as p value ≤ 0.05, I value < 0, and Z score > 0. Type IV LH was defined as p value ≤ 0.05, I value > 0, and Z score < 0. Type five LL was defined as p value ≤ 0 .05, I value < 0, and Z score < 0; Step S4: according to the obtained calculation results, the number of times that type 3 HL and type 4 LH appear in the calculation results of the six indicators for each monitoring point is counted. If the number of type 3 HL or type 4 LH is less than 1, it indicates that the six indicators of the monitoring point and its adjacent monitoring points have the same cluster and high correlation, and the monitoring point is deleted; otherwise, the point is retained to obtain a point set A={A1, A2, ..., An} that meets the actual measurement requirements; Step S5, reading the soil respiration data measured in the field corresponding to the obtained point set; Step S6, performing spatial interpolation according to the soil respiration data in step S5 to obtain predicted values ​​of other monitoring points until the soil respiration of the entire monitoring area is obtained; Step S7, output monitoring results.

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