Method and device for measuring grassland degradation by fusing multi-source remote sensing data and medium

Abstract

The invention provides a method, equipment and medium for measuring grassland degradation by fusing multi-source remote sensing data, and the method comprises the steps: selecting five kinds of land utilization data, namely MCD12Q1, CCI-LC, Globeland30, CNLUCC and CLCD, preprocessing the data, and superposing the five kinds of land utilization data to obtain grassland distribution and area time sequence data; comparing grassland distribution time sequence data, and selecting areas which are all grassland in the time sequence as constant grassland; six vegetation indexes of NDVI, EVI, LAI, SIF, NPP and GPP are selected, a Theil-Sen Media method is combined with Mann-Kendall inspection, the change trend of the six vegetation indexes in a time sequence is analyzed, and finally the grassland degradation area and area are determined. The method solves the problems of uncertainty of single land utilization data and remote sensing data and inconsistency among various data, and has the characteristics of simplicity in operation and high grassland degradation measurement precision. The method mainly solves the problem of grassland degradation determination, and provides a scientific basis for grassland degradation treatment and degraded grassland ecosystem restoration.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing technology, and in particular to a method, a device and a medium for measuring grassland degradation by fusing multi-source remote sensing data. Background Art

[0002] Grasslands are an important component of terrestrial ecosystems worldwide. Grasslands are not only essential for supporting ecosystem functions, services and the livelihoods of millions of people, but also play a key role in protecting biodiversity, climate regulation and global biogeochemical cycles.

[0003] However, over the past few decades, grasslands have been seriously degraded due to frequent human activities and environmental changes. Grassland degradation not only causes huge environmental problems such as soil erosion and land desertification, but also poses a huge threat to food, feed, fuel and medicine that humans rely on for survival. Therefore, grassland degradation has gradually become one of the key issues that need to be solved urgently. Due to the inconsistency of data between different land use data and the differences between different vegetation indices, it is difficult to obtain accurate information on the distribution and area of ​​grassland degradation. Summary of the invention

[0004] The first object of the present invention is to provide a method for measuring grassland degradation by fusing multi-source remote sensing data.

[0005] To this end, the above-mentioned purpose of the present invention is achieved through the following technical solutions:

[0006] A method for determining grassland degradation by fusing multi-source remote sensing data, the main steps include:

[0007] S1. Select five land use data, MCD12Q1, CCI-LC, Globeland30, CNLUCC and CLCD, and preprocess the data;

[0008] S2, superimposing the five land use data to obtain grassland distribution and area time series data;

[0009] Compare the grassland distribution time series data, and select the areas that are grasslands in this time series as constant grasslands;

[0010] S3. The Theil-Sen Median method combined with the Mann-Kendall test was used to analyze the changing trends of the six vegetation indices, NDVI, EVI, LAI, SIF, NPP and GPP, in the time series;

[0011] S4. Determine the grassland degradation region and area within the time series based on the changing trends of the six vegetation indices.

[0012] While adopting the above technical solutions, the present invention may also adopt or combine the following technical solutions:

[0013] As a preferred technical solution of the present invention: in step S1, the preprocessing process includes: unifying the resolution to 1 km and merging the grassland types in the five land use type data.

[0014] As a preferred technical solution of the present invention: in step S2, five types of land use data are superimposed in ArcGIS using overlay analysis.

[0015] As a preferred technical solution of the present invention: in step S2, the standard for selecting the grassland land use type is: at least three of the five land use type data show that the area is grassland.

[0016] As a preferred technical solution of the present invention: in step S3, the calculation methods of the Theil-Sen Median method and the Mann-Kendall test of the six vegetation indices of NDVI, EVI, LAI, SIF, NPP and GPP are as follows:

[0017] The calculation formula of Theil-Sen Median method is as follows:

[0018]

[0019] Among them, β represents the trend of vegetation index, Median() represents the median function, i and j represent time series, and x i and x j Represents the pixel value of the vegetation index at time i and j; when β>0, it means that the vegetation index of the pixel is on the rise; when β<0, it means that the vegetation index of the pixel is on the decline;

[0020] The Mann-Kendall (MK) test is a nonparametric test used to test the trend of time series. It is not affected by missing values ​​and outliers. The measurement results of this method do not need to obey the normal distribution and are suitable for significance tests of long time series. The test statistic S is calculated using the following formula:

[0021]

[0022] Where n is the number of data in the series, (x j -x i ) is the difference in vegetation index between adjacent years, and sgn() is the sign function, which can be calculated as:

[0023]

[0024] The trend test is performed using the test statistic Z, and the Z value is calculated as:

[0025]

[0026] Where Var is calculated by the following formula:

[0027]

[0028] Z follows a standard normal distribution; at a given significance level (α=0.05), when |Z|>1.96, it means that the trend has passed the significance test with a reliability of 95%.

[0029] As a preferred technical solution of the present invention: in step S4, the area where β<0 in the Mann-Kendall test results of each vegetation index is selected and determined to be a grassland with a degradation trend.

[0030] As a preferred technical solution of the present invention: in step S4, the grassland degradation area and the greening area can be determined according to the β and Z values ​​in the following Table 1:

[0031] Table 1 Classification of Mann-Kendall (MK) test parameters

[0032]

[0033] As a preferred technical solution of the present invention: in step S4, if one of the six vegetation indices shows a degradation trend, the grassland in the area is determined to be degraded.

[0034] As a preferred technical solution of the present invention: in step S4, the areas showing grassland degradation in the six vegetation indices are superimposed to obtain the grassland degradation distribution and area.

[0035] A second object of the present invention is to provide an electronic device based on the method described above.

[0036] To this end, the above-mentioned purpose of the present invention is achieved through the following technical solutions:

[0037] An electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus.

[0038] a memory for storing a computer program,

[0039] A processor is used to execute the computer program stored in the memory to implement the method steps of fusing multi-source remote sensing data to determine grassland degradation as described above.

[0040] Another object of the present invention is to provide a computer-readable storage medium based on the method described above.

[0041] To this end, the above-mentioned purpose of the present invention is achieved through the following technical solutions:

[0042] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method steps for determining grassland degradation by fusing multi-source remote sensing data as described above.

[0043] The present invention provides a method, device and medium for determining grassland degradation by fusing multi-source remote sensing data. The method comprises the following steps: selecting five land use data, namely MCD12Q1, CCI-LC, Globeland30, CNLUCC and CLCD, pre-processing the data, and superimposing the five land use data to obtain grassland distribution and area time series data; comparing grassland distribution time series data, selecting areas that are all grasslands in this time series as constant grasslands; selecting six vegetation indices, namely NDVI, EVI, LAI, SIF, NPP and GPP, using the Theil-Sen Median method combined with the Mann-Kendall test, analyzing the changing trends of the six vegetation indices in the time series, and finally determining the grassland degradation area and area. The present invention solves the problem of uncertainty of single land use data and remote sensing data and the inconsistency between multiple data, and has the characteristics of simple operation and high accuracy in determining grassland degradation. The present invention mainly solves the problem of grassland degradation determination, and provides a scientific basis for grassland degradation control and restoration of degraded grassland ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The present invention provides a flow chart of a method for determining grassland degradation by fusing multi-source remote sensing data. DETAILED DESCRIPTION

[0045] The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.

[0046] Combine the following Figure 1 A flow chart describing the method for determining grassland degradation by fusing multi-source remote sensing data provided by the present invention.

[0047] In this embodiment, it should be noted that the method for determining grassland degradation using multi-source remote sensing data includes the following steps:

[0048] S1. Land use data preprocessing

[0049] Five types of land use data, namely MCD12Q1, CCI-LC, Globeland30, CNLUCC and CLCD, were selected as shown in Table 2. The data were preprocessed, the resolution was unified to 1 km, and the grassland types in the five land use type data were merged.

[0050] Table 2 Comparison of basic information of five land use data

[0051]

[0052] S2. Determination of constant grassland area

[0053] Using overlay analysis in ArcGIS, five types of land use data were superimposed, and the criterion for selecting the grassland land use type was that at least three of the five land use type data showed that the area was grassland, and the constant grassland area was determined.

[0054] S3. Time series trend analysis of six vegetation index data

[0055] Six vegetation indices, NDVI, EVI, LAI, SIF, NPP and GPP, were selected for the Theil-Sen Median method and Mann-Kendall test. The basic information of the six vegetation indices is shown in Table 3.

[0056] Table 3 Comparison of basic information of six vegetation indices

[0057]

[0058]

[0059] The calculation formula of Theil-Sen Median method is as follows:

[0060]

[0061] Among them, β represents the trend of vegetation index, Median() represents the median function, i and j represent time series, and x i and x j Represents the pixel value of the vegetation index at time i and j; when β>0, it means that the vegetation index of the pixel is on an upward trend, and when β<0, it means that the vegetation index of the pixel is on a downward trend.

[0062] The Mann-Kendall (MK) test is a nonparametric test used to test the trend of time series and is not affected by missing values ​​and outliers. The measurement results of this method do not need to obey the normal distribution and are suitable for significance tests of long time series. The test statistic S is calculated using the following formula:

[0063]

[0064] Where n is the number of data in the series, (x j -x i ) is the difference in vegetation index between adjacent years, and sgn() is the sign function, which can be calculated as:

[0065]

[0066] The trend test is performed using the test statistic Z, and the Z value is calculated as:

[0067]

[0068] Where Var is calculated by the following formula:

[0069]

[0070] Z follows a standard normal distribution; at a given significance level (α=0.05), when |Z|>1.96, it means that the trend has passed the significance test, and its reliability is 95%. The grassland degradation and greening areas can be determined according to the β and Z values ​​in Table 1.

[0071] S4. Determine the grassland degradation area and area within the time series

[0072] In the Mann-Kendall test results of the six vegetation indices, the area with β<0 was judged to be showing a degradation trend. If one of the six vegetation indices showed a degradation trend, the grassland in the area could be judged to be degraded. The grassland degradation areas of the six vegetation indices were superimposed to obtain the distribution and area of ​​grassland degradation.

[0073] The present invention also provides an electronic device, the electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus.

[0074] a memory for storing a computer program,

[0075] A processor is used to execute the computer program stored in the memory to implement the method steps of fusing multi-source remote sensing data to determine grassland degradation as described above.

[0076] The present invention also provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method steps for determining grassland degradation by fusing multi-source remote sensing data as described above are implemented.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining grassland degradation by fusing multi-source remote sensing data, characterized in that: The steps include: S1. Select five land use data, namely MCD12Q1, CCI-LC, Globeland30, CNLUCC and CLCD, and pre-process the five land use data to merge the grassland types in the five land use type data; S2, superimpose the five land use data and obtain grassland distribution and area time series data; By comparing the grassland distribution time series data, the areas that were grasslands throughout the time series were selected and regarded as constant grasslands, so as to analyze the changing trends of the six vegetation indices; S3. The Theil-Sen Median method combined with the Mann-Kendall test was used to analyze the changing trends of the six vegetation indices, NDVI, EVI, LAI, SIF, NPP and GPP, in the time series; S4. Determine the grassland degradation region and area within the time series based on the changing trends of the six vegetation indices.

2. The method for determining grassland degradation by fusing multi-source remote sensing data according to claim 1, characterized in that: In step S1, the preprocessing process includes: unifying the resolution to 1 km and merging the grassland types in the five land use type data.

3. The method for determining grassland degradation by fusing multi-source remote sensing data according to claim 1, characterized in that: In step S2, the five land use data are superimposed in ArcGIS using overlay analysis.

4. The method for determining grassland degradation by fusing multi-source remote sensing data according to claim 1, characterized in that: In step S2, the standard for selecting the grassland land use type is that at least three of the five land use type data show that the area is grassland.

5. The method for determining grassland degradation by fusing multi-source remote sensing data according to claim 1, characterized in that: In step S3, the calculation methods of the Theil-Sen Median method and the Mann-Kendall test for the six vegetation indices of NDVI, EVI, LAI, SIF, NPP and GPP are as follows: The calculation formula of Theil-Sen Median method is as follows: Among them, β represents the trend of vegetation index, Median() represents the median function, i and j represent time series, and x i and x j Represents the pixel value of the vegetation index at time i and j; when β>0, it means that the vegetation index of the pixel is on the rise; when β<0, it means that the vegetation index of the pixel is on the decline; The Mann-Kendall (MK) test is a nonparametric test used to test the trend of time series. It is not affected by missing values ​​and outliers. The measurement results of this method do not need to obey the normal distribution and are suitable for significance tests of long time series. The test statistic S is calculated using the following formula: Where n is the number of data in the series, (x j -x i ) is the difference in vegetation index between adjacent years, and sgn() is the sign function, which can be calculated as: The trend test is performed using the test statistic Z, and the Z value is calculated as: Where Var is calculated by the following formula: Z follows a standard normal distribution; at a given significance level (α=0.05), when |Z|>1.96, it means that the trend has passed the significance test with a reliability of 95%.

6. The method for determining grassland degradation by fusing multi-source remote sensing data according to claim 1, characterized in that: In step S4, the areas where β<0 in the Mann-Kendall test results of each vegetation index are selected and determined to be grasslands showing a degradation trend.

7. The method for determining grassland degradation by fusing multi-source remote sensing data according to claim 1, characterized in that: In step S4, if one of the six vegetation indices shows a degradation trend, the grassland in the area is determined to be degraded.

8. The method for determining grassland degradation by fusing multi-source remote sensing data according to claim 1, characterized in that: In step S4, the areas showing grassland degradation in the six vegetation indices are superimposed to obtain the distribution and area of ​​grassland degradation.

9. An electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus, characterized in that: a memory for storing a computer program, A processor, wherein the processor is used to execute a computer program stored in a memory to implement the method steps of determining grassland degradation by fusing multi-source remote sensing data as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps for determining grassland degradation by fusing multi-source remote sensing data as described in any one of claims 1 to 8 are implemented.

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