CN110826578A - Remote sensing rapid extraction method of karst stony desertification information of global scale - Google Patents
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Abstract
The invention discloses a remote sensing quick extraction method of karst stony desertification information of a global scale, S1, compiling a carbonate distribution map of a southern karst area; s2, inverting the vegetation coverage of the universe by adopting a binary linear pixel decomposition model, and solving the problems of small coverage and poor contrast of other methods; s3, calculating the vegetation coverage evolution indexes among different years by using the vegetation coverage of the step S2 as a parameter and adopting a difference method; s4, removing non-stony special terrains such as water surfaces, towns and the like by using the mean value and the variance; s5, based on the vegetation coverage evolution index of the S4 removed special land types, establishing a karst stony desertification evolution conceptual model; and S6, establishing a karst stony desertification evolution mathematical model by combining the stony desertification background data on the basis of S5, and quickly extracting the full-domain scale karst stony desertification information. The global scale stony desertification data extracted by the method can provide basic data for comprehensive stony desertification control and serve ecological civilization construction of a karst area in the south.
Description
Technical Field
The invention relates to the technical field of remote sensing application, in particular to a remote sensing rapid extraction method of karst stony desertification information of a global scale.
Background
Karst stony desertification is a process and manifestation of the degradation of bare land in rocks in tropical-subtropical karst areas. China is a large karst country, the area of a tropical-subtropical karst area in south of Qinling mountain-Huaihe south reaches 60 ten thousand square kilometers, and the living population exceeds 4 hundred million. Karst stony desertification is the most main geological environment problem in the karst area in the south of China, is the source of disasters, poverty reasons and roots of laggard residents in the karst area, and seriously threatens the living environment of local people.
On the national strategic level, the karst stony desertification spatial distribution data of the global scale is the basis and the foundation for the nation to formulate the karst stony desertification control plan, is the first data for evaluating the control effect of the karst stony desertification, and is the basis for the 'integral protection, system repair and comprehensive control' work of deploying the national soil space in the karst region. On the aspect of scientific research, one of basic data of karst ecology and karst ecology in a global scale karst stony desertification space distribution data area is an important parameter for researching the structure, the function and the evolution formation of a typical karst ecosystem, and is a practical basis for solving key technical problems restricting the agricultural development of karst mountain areas.
The existing karst stony desertification remote sensing information extraction method has the following defects:
firstly, the coverage range is wide, the karst stony desertification remote sensing survey is concentrated in the karst region in the southwest at present, the system survey is not carried out on the karst regions in the south including Zhejiang province, Fujian province, Jiangxi province, Anhui province, southwest of Henan province, southern Shaanxi province and southern Gansu province, and the karst stony desertification data of the global scale of the karst regions in the south are lacked.
And secondly, the problem of poor contrast of survey results on a time scale and a space scale caused by remote sensing data of different sensors in different time phases is not solved, and the method is not suitable for karst stony desertification remote sensing survey in a global scale.
Thirdly, the investigation time is long, the updating period of the stony desertification data is slow, and a large amount of manpower, material resources and financial resources are consumed.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a remote sensing quick extraction method of karst stony desertification information on a global scale.
In order to achieve the purpose, the invention provides the following technical scheme: a remote sensing rapid extraction method of karst stony desertification information with global scale,
s1, compiling a carbonate rock distribution map of the southern karst area:
the carbonate rock is a material base for karst stony desertification, and the distribution map of the carbonate rock defines the working area range of the stony desertification investigation. According to regional geological data of the southern region, lithology is divided into three categories of pure carbonate rock, impure carbonate rock and non-carbonate rock according to rock mineral composition, structure and structure, a carbonate rock distribution diagram of the southern karst region is compiled according to spatial distribution of the pure carbonate rock and the impure carbonate rock, and the material basis and the spatial range of karst rock desertification investigation are determined;
s2, vegetation coverage (FVC) quantitative inversion:
selecting MODIS data covering the whole area of the southern karst area as a remote sensing data source, and combining the remote sensing data based on the maximum value of the NDVI synthetic value with the resolution of 250 meters in 6-10 months; the NDVI value of basic remote sensing data is taken as a parameter, a binary linear pixel decomposition model is adopted to quantitatively invert the vegetation coverage of the universe, the model is assumed to contain only vegetation and non-vegetation in the region, the range of 5% -95% of the cumulative frequency of the NDVI is selected as a confidence range of the FVC, the NDVI and the FVC are linearly related in the confidence range, and the expression of the vegetation coverage is obtained as follows:
in the formula NDVIminNDVI is the cumulative NDVI value at a frequency of 5%maxNDVI value when the cumulative frequency is 95%;
s3, calculating a vegetation coverage evolution index:
and S2, taking the FVC value obtained by the reverse performance of the remote sensing data of I as a parameter, and calculating the vegetation coverage evolution index between different years by adopting a difference method, wherein the specific expression is as follows:
in the formula, FVCd is a vegetation coverage evolution index from n years to m years, wherein m years are after n years, FVCmIs vegetation coverage of m years, FVCnVegetation coverage for n years;
s4, removing special types:
the 250-meter vegetation index 16-day synthetic data of MOD13Q1 in the 22-stage MODIS data of the whole year are converted into vegetation coverage according to the method of the step S2, and the mean value and the variance of the 22-stage vegetation coverage are calculated by pixel (for convenience of calculation and statistics, the mean value and the variance are calculated after the vegetation coverage is multiplied by 100). Pixels with the mean value smaller than 20 and the variance of 25 are removed (the pixels are mainly special non-stony desertification land types such as water surfaces, towns and the like), so that the purpose of removing the special land types is achieved;
s5, establishing a karst stony desertification evolution conceptual model:
the FVC after the special land type is removed by the step S4dValue grading, FVCdValue is at
Picture elements in the interval of-1, -0.6) are defined as severely deteriorated
Pixels in the interval of-0.6-0.2) are defined as mild deterioration
Picture elements in the interval of-0.2, 0.2) are defined as being substantially unchanged
Picture elements in the interval [0.2, 0.6) are defined as slightly improved
The picture element in the interval of [0.6, 1) is defined as better improvement;
s6, establishing a karst stony desertification evolution mathematical model, and rapidly extracting global scale karst stony desertification information:
converting the karst stony desertification evolution conceptual model into a mathematical model convenient for calculation by combining stony desertification background data, and concretely assigning no stony desertification in the current situation of n years of stony desertification as a positive integer 11, a slight stony desertification as a positive integer 13, a moderate stony desertification as a positive integer 15 and a severe stony desertification as a positive integer 17; evolution index of vegetation coverage (FVC) from n years to m yearsd) Severe exacerbations in the grading are assigned a positive integer of 1, mild exacerbations are assigned a positive integer of 3, substantially unchanged are assigned a positive integer of 5, mild improvement are assigned a positive integer of 7, better improvement are assigned a positive integer of 9; multiplying the two to obtain the stony desertification degree of m years.
Preferably, in step S2, the FVC value is calculated according to the binary linear pel decomposition model, wherein the pel FVC value of the NDVI value smaller than 5% of the accumulated frequency is assigned to 0, the pel FVC value of the NDVI value larger than 95% of the accumulated frequency is assigned to 1, the FVC value is a positive number between 0 and 1, and the small value of the calculated FVC value represents the degree of coverage of the plant.
Preferably, in step S4, according to the value range of FVC being 0 to 1, the corresponding value range of FVCd is-1 to 1, a negative value thereof represents a decrease in vegetation coverage, a value near 0 represents a substantial unchanged vegetation coverage, and a positive value represents an increase in vegetation coverage.
The invention provides a remote sensing rapid extraction method of global scale karst stony desertification information, which has the following beneficial effects:
1. the method selects MODIS data covering the same sensor and the same time phase of the universe of the karst region in the south as a remote sensing data source, inverts vegetation coverage by taking the maximum value of an NDVI synthetic value as a parameter, establishes vegetation coverage evolution indexes among different years, eliminates special land types, establishes a karst stony desertification evolution model by combining stony desertification background data, and rapidly extracts karst stony desertification information by taking the model as a basis, so that basic data are provided for comprehensive treatment and effect evaluation of the karst stony desertification, and ecological civilization construction is served.
2. The method has the advantages that the MODIS data of the same sensor and the same time phase covering the whole domain of the karst region in the south are selected to solve the two problems of coverage range and contrast at a stroke for a remote sensing data source, the established karst stony desertification evolution model can be quickly converted into a mathematical model, rapid calculation and processing by using a GIS are achieved, and rapid extraction of the karst stony desertification is achieved.
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FIG. 1 is a schematic flow chart of the steps of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit 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.
Example 1
S1, compiling a carbonate rock distribution map of the southern karst area:
the carbonate rock is a material base for karst stony desertification, and the distribution map of the carbonate rock defines the working area range of the stony desertification investigation. According to regional geological data of the southern region, lithology is divided into three categories of pure carbonate rock, impure carbonate rock and non-carbonate rock according to rock mineral composition, structure and structure, a carbonate rock distribution diagram of the southern karst region is compiled according to spatial distribution of the pure carbonate rock and the impure carbonate rock, and the material basis and the spatial range of karst rock desertification investigation are determined;
s2, vegetation coverage (FVC) quantitative inversion:
selecting MODIS data covering the whole area of the southern karst area as a remote sensing data source, and combining the remote sensing data based on the maximum value of the NDVI synthetic value with the resolution of 250 meters in 6-10 months; the NDVI value of basic remote sensing data is taken as a parameter, a binary linear pixel decomposition model is adopted to quantitatively invert the vegetation coverage of the universe, the model is assumed to contain only vegetation and non-vegetation in the region, the range of 5% -95% of the cumulative frequency of the NDVI is selected as a confidence range of the FVC, the NDVI and the FVC are linearly related in the confidence range, and the expression of the vegetation coverage is obtained as follows:
in the formula NDVIminNDVI is the cumulative NDVI value at a frequency of 5%maxNDVI value when the cumulative frequency is 95%;
s3, calculating a vegetation coverage evolution index:
and S2, taking the FVC value obtained by the reverse performance of the remote sensing data of I as a parameter, and calculating the vegetation coverage evolution index between different years by adopting a difference method, wherein the specific expression is as follows:
in the formula, FVCd is a vegetation coverage evolution index from n years to m years, wherein m years are after n years, FVCmIs vegetation coverage of m years, FVCnVegetation coverage for n years;
s4, removing special types:
the 250-meter vegetation index 16-day synthetic data of MOD13Q1 in the 22-stage MODIS data of the whole year are converted into vegetation coverage according to the method of the step S2, and the mean value and the variance of the 22-stage vegetation coverage are calculated by pixel (for convenience of calculation and statistics, the mean value and the variance are calculated after the vegetation coverage is multiplied by 100). Pixels with the mean value smaller than 20 and the variance of 25 are removed (the pixels are mainly special non-stony desertification land types such as water surfaces, towns and the like), so that the purpose of removing the special land types is achieved;
s5, establishing a karst stony desertification evolution conceptual model:
the FVC after the special land type is removed by the step S4dValue grading, FVCdValue is at
Picture elements in the interval of-1, -0.6) are defined as severely deteriorated
Pixels in the interval of-0.6-0.2) are defined as mild deterioration
Picture elements in the interval of-0.2, 0.2) are defined as being substantially unchanged
Picture elements in the interval [0.2, 0.6) are defined as slightly improved
The picture element in the interval of [0.6, 1) is defined as better improvement;
s6, establishing a karst stony desertification evolution mathematical model, and rapidly extracting global scale karst stony desertification information:
the karst stony desertification evolution conceptual model is converted into a mathematical model convenient for calculation by combining stony desertification background data, and particularly, the stony desertification-free conceptual model and the light stony desertification conceptual model in the current situation of n years of stony desertification are assigned as positive integers of 11 and positive integersInteger 13, moderate stony desertification is assigned as positive integer 15, and severe stony desertification is assigned as positive integer 17; evolution index of vegetation coverage (FVC) from n years to m yearsd) Severe exacerbations in the grading are assigned a positive integer of 1, mild exacerbations are assigned a positive integer of 3, substantially unchanged are assigned a positive integer of 5, mild improvement are assigned a positive integer of 7, better improvement are assigned a positive integer of 9; multiplying the two to obtain the stony desertification degree of m years.
Example 2
S1, compiling a carbonate rock distribution map of the southern karst area:
the carbonate rock is a material base for karst stony desertification, and the distribution map of the carbonate rock defines the working area range of the stony desertification investigation. According to regional geological data of the southern region, lithology is divided into three categories of pure carbonate rock, impure carbonate rock and non-carbonate rock according to rock mineral composition, structure and structure, a carbonate rock distribution diagram of the southern karst region is compiled according to spatial distribution of the pure carbonate rock and the impure carbonate rock, and the material basis and the spatial range of karst rock desertification investigation are determined;
s2, vegetation coverage (FVC) quantitative inversion:
selecting MODIS data covering the whole area of the southern karst area as a remote sensing data source, and combining the remote sensing data based on the maximum value of the NDVI synthetic value with the resolution of 250 meters in 6-10 months; the NDVI value of basic remote sensing data is taken as a parameter, a binary linear pixel decomposition model is adopted to quantitatively invert the vegetation coverage of the universe, the model is assumed to contain only vegetation and non-vegetation in the region, the range of 5% -95% of the cumulative frequency of the NDVI is selected as a confidence range of the FVC, the NDVI and the FVC are linearly related in the confidence range, and the expression of the vegetation coverage is obtained as follows:
in the formula NDVIminNDVI is the cumulative NDVI value at a frequency of 5%maxNDVI value when the cumulative frequency is 95%;
s3, calculating a vegetation coverage evolution index:
and S2, taking the FVC value obtained by the reverse performance of the remote sensing data of I as a parameter, and calculating the vegetation coverage evolution index between different years by adopting a difference method, wherein the specific expression is as follows:
in the formula, FVCdIs vegetation coverage evolution index from 2008 to 2018, FVC2018Is the vegetation coverage of 2018, FVC2008Vegetation coverage in 2008;
s4, removing special types:
the 250-meter vegetation index 16-day synthetic data of MOD13Q1 in the 22-stage MODIS data of the whole year are converted into vegetation coverage according to the method of the step S2, and the mean value and the variance of the 22-stage vegetation coverage are calculated by pixel (for convenience of calculation and statistics, the mean value and the variance are calculated after the vegetation coverage is multiplied by 100). Pixels with the mean value smaller than 20 and the variance of 25 are removed (the pixels are mainly special non-stony desertification land types such as water surfaces, towns and the like), so that the purpose of removing the special land types is achieved;
s5, establishing a karst stony desertification evolution conceptual model:
the FVC after the special land type is removed by the step S4dValue grading, FVCdValue is at
Picture elements in the interval of-1, -0.6) are defined as severely deteriorated
Pixels in the interval of-0.6-0.2) are defined as mild deterioration
Picture elements in the interval of-0.2, 0.2) are defined as being substantially unchanged
Picture elements in the interval [0.2, 0.6) are defined as slightly improved
The picture element in the interval of [0.6, 1) is defined as better improvement;
the current situation of stony desertification in 2008 includes no stony desertification, mild stony desertification, moderate stony desertification and severe stony desertification, and based on the background data, a karst stony desertification evolution conceptual model is established based on a vegetation coverage evolution index, as shown in table 1,
TABLE 1 concept model of karst stony desertification evolution
As can be seen from table 1, the first column and the second column of the concept model of karst stony desertification evolution are the current conditions of stony desertification in 2008, including no stony desertification, mild stony desertification, moderate stony desertification and severe stony desertification; vegetation coverage evolution index (FVC) between 2008 and 2018 for first and second behaviorsd) Grading, including severe exacerbation, mild exacerbation, substantially unchanged, mild improvement, better improvement; each current condition of stony desertification corresponds to 5-plant coverage evolution index grading, thereby determining the degree of stony desertification in 2018 (starting from the third row and column, italics in the table).
S6, fast extraction of full-scale karst stony desertification:
in order to rapidly extract karst stony desertification information of a universe scale in a GIS, a karst stony desertification evolution conceptual model needs to be converted into a mathematical model convenient for calculation, and particularly, the current situation of the stony desertification in 2008 is assigned with a positive integer of 11, the light stony desertification is assigned with a positive integer of 13, the medium stony desertification is assigned with a positive integer of 15 and the heavy stony desertification is assigned with a positive integer of 17; the vegetation coverage evolution index (FVC) from 2008 to 2018d) Severe exacerbations in the grading are assigned a positive integer of 1, mild exacerbations are assigned a positive integer of 3, substantially unchanged are assigned a positive integer of 5, mild improvement are assigned a positive integer of 7, better improvement are assigned a positive integer of 9; multiplying the two results in the extent of stony desertification of 2018 (starting from the third row and column, italic numbers in Table 2)
TABLE 2 karst stony desertification evolution mathematic model
From table 2, the patches with the calculation results of 13, 15, 17, 45 and 51 are severe stony desertification, the patches with the calculation results of 11, 39, 75 and 85 are moderate stony desertification, the patches with the calculation results of 33, 65, 105 and 119 are mild stony desertification, the patches with the calculation results of 55, 77, 91, 99, 117, 135 and 153 are non-stony desertification, and the karst stony desertification current state graph of 2018 years is compiled according to the extracted karst stony desertification information, so that basic data are provided for comprehensive control and effect evaluation of the karst stony desertification.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (3)
1. The remote sensing rapid extraction method of the global scale karst stony desertification information is characterized by comprising the following steps: the method comprises the following steps:
s1, compiling a carbonate rock distribution map of the southern karst area:
according to regional geological data of the southern region, compiling a carbonate distribution map of the southern karst region according to rock mineral components, structures and structures;
s2, vegetation coverage (FVC) quantitative inversion:
selecting MODIS data covering the universe of the southern karst area as a remote sensing data source, and quantitatively inverting the vegetation coverage of the universe by adopting a binary linear pixel decomposition model to obtain a vegetation coverage expression as follows:
in the formula NDVIminNDVI is the cumulative NDVI value at a frequency of 5%maxNDVI value when the cumulative frequency is 95%;
s3, calculating a vegetation coverage evolution index:
and S2, taking the FVC value obtained by the reverse performance of the remote sensing data of I as a parameter, and calculating the vegetation coverage evolution index between different years by adopting a difference method, wherein the specific expression is as follows:
in the formula, FVCdIs a vegetation coverage evolution index of n to m years, where m years are after n years, FVCmIs vegetation coverage of m years, FVCnVegetation coverage for n years;
s4, removing special types:
removing special non-stony desertification land types such as water surfaces, towns and the like by taking the mean value and the variance of the vegetation coverage in the 22 th period every year as parameters;
s5, establishing a karst stony desertification evolution conceptual model:
the FVC after the special land type is removed by the step S4dValue grading, FVCdValue is at
Picture elements in the interval of-1, -0.6) are defined as severely deteriorated
Pixels in the interval of-0.6-0.2) are defined as mild deterioration
Picture elements in the interval of-0.2, 0.2) are defined as being substantially unchanged
Picture elements in the interval [0.2, 0.6) are defined as slightly improved
The picture element in the interval of [0.6, 1) is defined as better improvement;
s6, establishing a karst stony desertification evolution mathematical model, and realizing fast extraction of the full-domain scale karst stony desertification:
converting the karst stony desertification evolution conceptual model into a mathematical model convenient for calculation, and particularly assigning the current situation of n years of stony desertification as a positive integer 11, a mild stony desertification as a positive integer 13, a moderate stony desertification as a positive integer 15 and a severe stony desertification as a positive integer 17; evolution index of vegetation coverage (FVC) from n years to m yearsd) Severe exacerbations in the grading are assigned a positive integer of 1, mild exacerbations are assigned a positive integer of 3, substantially unchanged are assigned a positive integer of 5, mild improvement are assigned a positive integer of 7, better improvement are assigned a positive integer of 9; multiplying the two to obtain the stony desertification degree of m years.
2. The global scale karst stony desertification information remote sensing rapid extraction method according to claim 1, characterized in that: in step S2, an FVC value is calculated according to the binary linear pixel decomposition model, wherein a pixel FVC value smaller than the NDVI value of 5% of the cumulative frequency is assigned to 0, a pixel FVC value larger than the NDVI value of 95% of the cumulative frequency is assigned to 1, the FVC value is a positive number between 0 and 1, and a small value of the calculated FVC value represents the degree of coverage of the plant.
3. The global scale karst stony desertification information remote sensing rapid extraction method according to claim 1, characterized in that: in step S4, according to the value range of FVC being 0 to 1, the corresponding FVCdThe value range of (1) is-1 to 1, wherein a negative value represents that the vegetation coverage is reduced, a value close to 0 represents that the vegetation coverage is basically unchanged, and a positive value represents that the vegetation coverage is increased.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150294154A1 (en) * | 2014-04-15 | 2015-10-15 | Open Range Consulting | System and method for assessing riparian habitats |
| CN107341492A (en) * | 2017-06-01 | 2017-11-10 | 中国科学院地球化学研究所 | A kind of Karst Rocky Desertification information extracting method |
| CN107909607A (en) * | 2017-12-11 | 2018-04-13 | 河北省科学院地理科学研究所 | A kind of year regional vegetation coverage computational methods |
| CN108593505A (en) * | 2018-04-18 | 2018-09-28 | 贵州大学 | A kind of analysis method of karst soil underground leakage |
| CN109122173A (en) * | 2018-09-12 | 2019-01-04 | 贵州大学 | A method of row stony desertification ecological vegetation, which is lunged, using tiger restores |
| CN110059553A (en) * | 2019-03-13 | 2019-07-26 | 中国科学院遥感与数字地球研究所 | The method for knowing potential landslide stage vegetation anomalies feature is sentenced using optical remote sensing image |
-
2019
- 2019-11-14 CN CN201911112363.3A patent/CN110826578B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150294154A1 (en) * | 2014-04-15 | 2015-10-15 | Open Range Consulting | System and method for assessing riparian habitats |
| CN107341492A (en) * | 2017-06-01 | 2017-11-10 | 中国科学院地球化学研究所 | A kind of Karst Rocky Desertification information extracting method |
| CN107909607A (en) * | 2017-12-11 | 2018-04-13 | 河北省科学院地理科学研究所 | A kind of year regional vegetation coverage computational methods |
| CN108593505A (en) * | 2018-04-18 | 2018-09-28 | 贵州大学 | A kind of analysis method of karst soil underground leakage |
| CN109122173A (en) * | 2018-09-12 | 2019-01-04 | 贵州大学 | A method of row stony desertification ecological vegetation, which is lunged, using tiger restores |
| CN110059553A (en) * | 2019-03-13 | 2019-07-26 | 中国科学院遥感与数字地球研究所 | The method for knowing potential landslide stage vegetation anomalies feature is sentenced using optical remote sensing image |
Non-Patent Citations (1)
| Title |
|---|
| 涂杰楠等: "基于高分辨率遥感影像的石漠化演变趋势分析——以蒙自东山生态治理区为例", 《中国岩溶》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113076796A (en) * | 2021-02-08 | 2021-07-06 | 中国科学院地理科学与资源研究所 | Karst stony desertification remote sensing mapping method and device |
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