CN117409313B - Sentinel-2 optical image-based method for constructing index of spartina alterniflora in weathering period - Google Patents
Sentinel-2 optical image-based method for constructing index of spartina alterniflora in weathering period Download PDFInfo
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Abstract
The invention relates to a method for constructing a spartina alterniflora weather decay period index based on a Sentinel-2 optical image, which comprises the following steps: selecting spartina alterniflora sample points in a research area, constructing an annual NDVI time sequence, and determining the fading period of spartina alterniflora; acquiring a high-quality image of the Sentinel-2 in the decay phase; acquiring a spectrum mean curve of each ground object based on a sample point, and selecting a sensitive wave band of the spectrum mean curve to construct a spartina alterniflora index; performing density segmentation on the calculated result of the spartina alterniflora index band; and filtering and masking to obtain the final spartina alterniflora extraction result. The beneficial effects of the invention are as follows: the invention can rapidly and simply obtain the large-scale spartina alterniflora distribution data set, is beneficial to scientifically and accurately monitoring the space-time dynamics of spartina alterniflora, and provides data support and decision reference for the control action of spartina alterniflora.
Description
Technical Field
The invention relates to the technical field of remote sensing, in particular to a method for constructing an index of a alternanthera philoxeroides weathered decay period based on a Sentinel-2 optical image.
Background
The spartina alterniflora is rapidly expanded in coastal areas of China since the introduction of the spartina alterniflora in the 70 th century, and becomes the external invasive plant with the biggest harm on coastal beaches of China. The invasion of the spartina alterniflora not only extrudes the living space of other plants to destroy the habitat of benthos, fishes and birds and change the structure of the coastal beach ecological system, so that the degradation of the coastal wetland ecological system and the reduction of biological diversity are caused, the safety of the coastal wetland ecological system in China is seriously threatened, but also the normal flow of tidal water is hindered, the flood discharge capacity of the river entering the sea is reduced, the production and the living of people are influenced, and the sustainable development of the economic society in coastal areas is restricted.
Scientific and accurate monitoring is a foundation and premise for preventing and controlling spartina alterniflora, but the spartina alterniflora is wide in distribution, spans a latitude range of 20-39 degrees N in China, has a special growth environment, is distributed from a low tide level to an average high tide level in an intertidal zone, and is difficult to carry out large-scale manual monitoring. The remote sensing technology can monitor spartina alterniflora in a large area, rapidly and accurately, but the existing research is insufficient. On the one hand, the spartina alterniflora is distributed in coastal areas, and is limited by tide level, weather conditions and the like, so that effective optical remote sensing data covered for a long time are difficult to obtain; on the other hand, the spartina alterniflora is easy to be confused with other wetland salt biogas vegetation, and has serious homospectrum and foreign matter homospectrum phenomena, so that the accurate identification is difficult only by utilizing the spectrum information of multiband data.
The conjugate can rapidly extract a large range of target ground objects by utilizing the spectrum index. A plurality of vegetation indexes such as NDVI and EVI are used for vegetation drawing so far, and compared with the traditional image classification method, the vegetation indexes are independent of a large number of sample selections and complex classifiers, so that the calculation efficiency is high and the generalization capability is high. However, no specific index is currently available for the single vegetation spartina alterniflora, so spartina alterniflora cannot be effectively distinguished from other vegetation. The existing spartina alterniflora indexes are mostly developed aiming at local areas, and no index can be used for nationally extracting spartina alterniflora in a large range.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art, and provides a method for constructing an index of a alternanthera philoxeroides weather decay period based on a Sentinel-2 optical image.
In a first aspect of the present invention, the invention provides a method for constructing a spartina alterniflora weather decay period index based on a Sentinel-2 optical image, which comprises the following steps:
Step1, selecting spartina alterniflora sample points in a research area by utilizing high-resolution images, constructing an annual NDVI time sequence, and determining the fading period of the spartina alterniflora;
step 2, downloading a Sentinel-2 image, and preprocessing to obtain a high-quality image of the Sentinel-2 in the decay period;
Step 3, obtaining spectrum mean curves of all features based on sample points, and selecting sensitive wave bands of the spectrum mean curves to construct spartina alterniflora indexes;
step 4, determining a threshold range of spartina alterniflora extraction based on the distribution of the sample point statistic box diagram, performing density segmentation on the spartina alterniflora index band calculation result, and converting the raster data after the density segmentation into vector data;
and 5, taking geographical habitat of spartina alterniflora into consideration, performing spatial superposition on the intertidal zone vector boundary data and the extraction result in the step 4, and performing filtering masking on the result in the step 4 to exclude areas which do not meet the growth requirement of spartina alterniflora, so as to obtain a final spartina alterniflora extraction result.
Preferably, in step 2, the preprocessing includes orthographic correction and sub-pel level geometric fine correction.
Preferably, in step 3, obtaining sample points of spartina alterniflora and other wetland vegetation, counting a spectrum mean value curve of the sample points, analyzing the difference between the spartina alterniflora and the other wetland vegetation spectrum curves, determining an optimal wave band, and then carrying out combined calculation to construct a spartina alterniflora index, wherein the formula is as follows:
wherein Index S.alterniflora represents the spartina alterniflora Index, RED represents the reflectance value of the wavelength band with the center wavelength of 665nm, redEdge represents the reflectance value of the wavelength band with the center wavelength of 865nm, and SWIR represents the reflectance value of the wavelength band with the center wavelength of 1374 nm.
Preferably, in step 4, the extraction threshold range is determined by a sample point statistical box diagram, and when the following threshold is met, the growth area of spartina alterniflora is obtained, and the expression is:
min<index<max
where min and max represent the lower and upper limits of the threshold.
In a second aspect, a system for constructing a alternanthera philoxeroides weathered degradation period index based on a Sentinel-2 optical image is provided, the method for constructing the index of the alternanthera philoxeroides weather decay period based on the Sentinel-2 optical image according to any one of the first aspect comprises the following steps:
The construction module is used for selecting spartina alterniflora sample points in the research area by utilizing the high-resolution images, constructing an annual NDVI time sequence and determining the fading period of the spartina alterniflora;
The preprocessing module is used for downloading the Sentinel-2 image, preprocessing the image and obtaining a high-quality image of the Sentinel-2 in the decay period;
The acquisition module is used for acquiring the spectrum mean value curve of each ground object based on the sample points and selecting a spectrum mean value curve sensitive wave band to construct an spartina alterniflora index;
The determining module is used for determining a threshold range of spartina alterniflora extraction based on the distribution of the sample point statistic box diagram, performing density segmentation on the spartina alterniflora index band calculation result, and converting the raster data after the density segmentation into vector data;
And the mask module is used for taking geographical habitat of spartina alterniflora into consideration, carrying out spatial superposition on the vector boundary data of the intertidal zone and the extraction result of the determination module, filtering the mask on the result of the determination module, removing the area which does not meet the growth requirement of spartina alterniflora, and obtaining the final spartina alterniflora extraction result.
In a third aspect, a computer storage medium having a computer program stored therein is provided; when the computer program runs on a computer, the computer is caused to execute the method for constructing the index of the alternanthera philoxeroides weather decay period based on the Sentinel-2 optical image according to any one of the first aspect.
The beneficial effects of the invention are as follows:
According to the method, firstly, a high-quality image in a decay period is obtained according to the climatic characteristics of spartina alterniflora, secondly, a vegetation spectrum mean value curve is obtained through sample points, an optimal wave band is determined to construct an extraction index, then an extraction threshold value of spartina alterniflora is determined by combining an index statistical result of a sample, and finally, false sub-pixels after threshold segmentation are removed by combining an intertidal vector boundary, so that accurate extraction of spartina alterniflora is realized. The invention fully plays the roles of vegetation weathers and geographical priori knowledge, and combines the spectrum features to realize accurate and rapid extraction of spartina alterniflora information by utilizing remote sensing images. The method can quickly and simply obtain a large-scale spartina alterniflora distribution data set, is beneficial to scientifically and accurately monitoring the space-time dynamics of spartina alterniflora, and provides data support and decision reference for the control action of spartina alterniflora. Therefore, the method provided by the invention has important practical application value.
Drawings
FIG. 1 is a flow chart of a method for constructing a weather-decay period index of spartina alterniflora based on a Sentinel-2 optical image;
FIG. 2 is a box plot of different terrain statistics for a typical distribution area of spartina alterniflora;
fig. 3 is a schematic diagram of the result of spartina alterniflora extraction.
Detailed Description
The invention is further described below with reference to examples. The following examples are presented only to aid in the understanding of the invention. It should be noted that it will be apparent to those skilled in the art that modifications can be made to the present invention without departing from the principles of the invention, and such modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.
Example 1:
As shown in fig. 1, embodiment 1 of the application provides a method for constructing an index of a spartina alterniflora weathered period based on a Sentinel-2 optical image, which fully utilizes the difference of the spartina alterniflora weathered period from other salt-marsh vegetation according to the weathered characteristics of the spartina alterniflora, highlights the difference of the spectral characteristics of the spartina alterniflora and other salt-marsh vegetation, and constructs the index for highlighting the spartina alterniflora information. The index aims to provide technical support for realizing large-area spartina alterniflora extraction nationwide, the index which is favorable for extracting spartina alterniflora is constructed through the spectral curve characteristics, the optimal threshold is determined according to the box diagram distribution, and further the fine extraction of the spartina alterniflora is realized through threshold segmentation.
The method provided by the invention not only fully utilizes the advantages of the time resolution and the space resolution of the Sentinel-2 data, is continuous in time and high in space resolution, is suitable for vegetation extraction in cloudy weather of coastal wetlands, but also considers the climatic characteristics of vegetation, fully exerts the advantages of the climatic characteristics, and is beneficial to realizing the large-scale spartina alterniflora extraction requirement. Specifically, the method provided by the invention comprises the following steps:
step1, selecting spartina alterniflora sample points in a research area by utilizing high-resolution images, constructing an annual NDVI time sequence, and determining the fading period of the spartina alterniflora.
And step 2, downloading the Sentinel-2 image, and preprocessing to obtain a high-quality image of the Sentinel-2 in the decay period.
In step 2, the preprocessing includes orthographic correction and sub-pixel level geometric fine correction.
And step 3, acquiring a spectrum mean curve of each ground object based on the sample points, and selecting a sensitive wave band of the spectrum mean curve to construct an spartina alterniflora index.
Specifically, obtaining spartina alterniflora and other wetland vegetation sample points, counting a spectrum mean value curve of the sample points, analyzing the difference of the spartina alterniflora and other wetland vegetation spectrum curves, determining an optimal wave band, then carrying out combined calculation to construct a spartina alterniflora index, wherein the formula is as follows:
wherein Index S.alterniflora represents the spartina alterniflora Index, RED represents the reflectance value of the wavelength band with the center wavelength of 665nm, redEdge represents the reflectance value of the wavelength band with the center wavelength of 865nm, and SWIR represents the reflectance value of the wavelength band with the center wavelength of 1374 nm.
And 4, determining a threshold range of spartina alterniflora extraction based on the distribution of the sample point statistic box diagram, performing density segmentation on the spartina alterniflora index band calculation result, and converting the raster data after the density segmentation into vector data.
Specifically, determining an extraction threshold range through a sample point statistical box diagram, and when the following threshold is met, namely the growing area of spartina alterniflora, representing as follows:
min<index<max
where min and max represent the lower and upper limits of the threshold.
And 5, taking geographical habitat of the spartina alterniflora into consideration, carrying out spatial superposition on the intertidal zone vector boundary data and the extraction result in the step 4, and filtering and Masking the result in the step 4 in ENVI software through a Masking tool to exclude the area which does not meet the growth requirement of the spartina alterniflora, so as to obtain the final spartina alterniflora extraction result.
Example 2:
On the basis of the embodiment 1, the embodiment 2 of the application provides a more specific method for constructing the index of the alternanthera philoxeroides weather decay period based on the Sentinel-2 optical image, which comprises the following steps:
Step 1, selecting spartina alterniflora sample points in a research area by utilizing Google Earth Pro high-resolution images, constructing an annual NDVI time sequence, and determining the decay period of spartina alterniflora.
The plant species usually show unique physical characteristics and are easy to distinguish, and the decay period of the spartina alterniflora is generally one to two months later than that of the local plant species, so that the invention selects spartina alterniflora sample points in a research area through Google Earth Pro high-resolution images, constructs spartina alterniflora NDVI time sequence data by using a time sequence harmonic analysis method on a GEE cloud computing platform to reflect the periodical change rule of vegetation, and further determines the decay period of the spartina alterniflora in each research area.
And 2, preprocessing cloud quantity, position and the like of the Sentinel-2 image on a Google EARTH ENGINE (GEE) platform, and obtaining a high-quality image of the Sentinel-2 in the decay period after orthographic correction and sub-pixel level geometric fine correction.
Specifically, a 'CLOUDY _PIXEL_ PERCENTAGE' function is utilized to screen Sentinel-2 images with the cloud content less than 1%, and preprocessing of time, position, tide level, cloud content and the like of the images is completed by depending on the GEE platform, so that high-quality images in the 2020-year recession period of each research area after orthographic correction and sub-PIXEL level geometric fine correction are obtained.
And step 3, acquiring a spectrum mean curve of each ground object based on the sample points, and further analyzing a sensitive wave band of the spectrum curve to construct an spartina alterniflora index.
Specifically, in ENVI software, spectral mean curves of 3 main land coverage types (i.e., spartina alterniflora, mangrove/reed, beach) sample points of each research area are calculated, and characteristic differences of the spectral mean curves of all features are analyzed to determine a sensitivity band to construct spartina alterniflora index, and although the spectral mean curves of the spartina alterniflora and other vegetation of 6 research areas are different, the characteristics for constructing the spartina alterniflora index are consistent.
And 4, determining a threshold range of spartina alterniflora extraction based on the distribution of the sample point statistic box diagram, and performing density segmentation on the spartina alterniflora index band calculation result.
Specifically, performing Band Math operation on the constructed spartina alterniflora index in ENVI software to obtain a spartina alterniflora index Band calculation result; then extracting the wave band calculation result to a corresponding sample point in ArcGIS software; the threshold range of spartina alterniflora extraction is determined through the distribution of the sample point statistic box diagram, and as can be seen from fig. 2, the spartina alterniflora index constructed by the invention can well enhance the difference between spartina alterniflora and other background information; and then extracting the calculated result of the spartina alterniflora exponential band through a density segmentation operation in ENVI software, wherein the abnormal value of a sample point in a box diagram is not considered when determining a threshold value.
And 5, taking geographical habitat of spartina alterniflora into consideration, and masking the extraction result in the step 4 by combining with the intertidal zone vector boundary to finally obtain the spartina alterniflora extraction result.
Specifically, 6 typical research areas are selected under different latitudes of coastal China, and the areas are, in order from north to south, shandong yellow river mouth, jiangsu salt city, shanghai Jiujia sand, zhejiang Sanmen Bay, fujian Luoyuan Bay and Guangxi North Bay. Because the spartina alterniflora is a salt marsh vegetation, the spartina alterniflora is usually grown in the intertidal zone and the estuary area of coastal, the extraction result of the step 4 is masked by combining the intertidal zone vector boundary to remove the false split pixels, and finally the spartina alterniflora extraction result is obtained. As can be seen from fig. 3, the present invention performs well in 6 research areas under different latitude conditions in coastal China.
In this embodiment, the same or similar parts as those in embodiment 1 may be referred to each other, and will not be described in detail in the present disclosure.
Example 3:
based on embodiments 1 and 2, embodiment 3 of the present application provides a system for constructing an index of a recession period of spartina alterniflora material based on a Sentinel-2 optical image, comprising:
The construction module is used for selecting spartina alterniflora sample points in the research area by utilizing the high-resolution images, constructing an annual NDVI time sequence and determining the fading period of the spartina alterniflora;
The preprocessing module is used for downloading the Sentinel-2 image, preprocessing the image and obtaining a high-quality image of the Sentinel-2 in the decay period;
The acquisition module is used for acquiring the spectrum mean value curve of each ground object based on the sample points and selecting a spectrum mean value curve sensitive wave band to construct an spartina alterniflora index;
The determining module is used for determining a threshold range of spartina alterniflora extraction based on the distribution of the sample point statistic box diagram and performing density segmentation on the spartina alterniflora index band calculation result; converting the raster data after density segmentation into vector data;
And the mask module is used for taking geographical habitat of spartina alterniflora into consideration, carrying out spatial superposition on the vector boundary data of the intertidal zone and the extraction result of the determination module, filtering the mask on the result of the determination module, removing the area which does not meet the growth requirement of spartina alterniflora, and obtaining the final spartina alterniflora extraction result.
Specifically, the system provided in this embodiment is a system corresponding to the method provided in embodiments 1 and 2, so that the portions in this embodiment that are the same as or similar to those in embodiments 1 and 2 may be referred to each other, and will not be described in detail in this disclosure.
In conclusion, the invention fully plays the roles of vegetation climate characteristics and geographical priori knowledge, and combines the spectrum characteristics to realize accurate and rapid extraction of spartina alterniflora information by utilizing remote sensing images. The method can quickly and simply obtain a large-scale spartina alterniflora distribution data set, is beneficial to scientifically and accurately monitoring the space-time dynamics of spartina alterniflora, and provides data support and decision reference for the control action of spartina alterniflora.
Claims (5)
1. The method for constructing the alternanthera philoxeroides weathered-period index based on the Sentinel-2 optical image is characterized by comprising the following steps of:
Step1, selecting spartina alterniflora sample points in a research area by utilizing high-resolution images, constructing an annual NDVI time sequence, and determining the fading period of the spartina alterniflora;
step 2, downloading a Sentinel-2 image, and preprocessing to obtain a high-quality image of the Sentinel-2 in the decay period;
Step 3, obtaining spectrum mean curves of all features based on sample points, and selecting sensitive wave bands of the spectrum mean curves to construct spartina alterniflora indexes;
In the step 3, obtaining spartina alterniflora and other wetland vegetation sample points, counting a spectrum mean value curve of the sample points, analyzing the difference between the spartina alterniflora and other wetland vegetation spectrum curves, determining an optimal wave band, then carrying out combined calculation, and constructing a spartina alterniflora index, wherein the formula is as follows:
wherein Index s.alterniflora represents the spartina alterniflora Index, RED represents the reflectance value of the wavelength band with the center wavelength of 665nm, RED Edge represents the reflectance value of the wavelength band with the center wavelength of 865nm, and SWIR represents the reflectance value of the wavelength band with the center wavelength of 1374 nm;
step 4, determining a threshold range of spartina alterniflora extraction based on the distribution of the sample point statistic box diagram, performing density segmentation on the spartina alterniflora index band calculation result, and converting the raster data after the density segmentation into vector data;
and 5, taking geographical habitat of spartina alterniflora into consideration, performing spatial superposition on the intertidal zone vector boundary data and the extraction result in the step 4, and performing filtering masking on the result in the step 4 to exclude areas which do not meet the growth requirement of spartina alterniflora, so as to obtain a final spartina alterniflora extraction result.
2. The method for constructing the index of the weather degradation period of spartina alterniflora based on the Sentinel-2 optical image according to claim 1, wherein in the step 2, the preprocessing comprises orthographic correction and sub-pixel level geometric fine correction.
3. The method for constructing the alternanthera philoxeroides weathered phase index based on the Sentinel-2 optical image according to claim 2, wherein in the step 4, the extraction threshold range is determined through a sample point statistics box diagram, and when the following threshold is met, the growth area of the alternanthera philoxeroides is represented as:
min＜index＜max
where min and max represent the lower and upper limits of the threshold.
4. A system for constructing a spartina alterniflora weathered-period index based on a Sentinel-2 optical image is characterized in that, a method for performing the Sentinel-2 optical image-based method for constructing the alternanthera philoxeroides weathered phase index of any one of claims 1 to 3, comprising:
The construction module is used for selecting spartina alterniflora sample points in the research area by utilizing the high-resolution images, constructing an annual NDVI time sequence and determining the fading period of the spartina alterniflora;
The preprocessing module is used for downloading the Sentinel-2 image, preprocessing the image and obtaining a high-quality image of the Sentinel-2 in the decay period;
The acquisition module is used for acquiring the spectrum mean value curve of each ground object based on the sample points and selecting a spectrum mean value curve sensitive wave band to construct an spartina alterniflora index;
The determining module is used for determining a threshold range of spartina alterniflora extraction based on the distribution of the sample point statistic box diagram, performing density segmentation on the spartina alterniflora index band calculation result, and converting the raster data after the density segmentation into vector data;
And the mask module is used for taking geographical habitat of spartina alterniflora into consideration, carrying out spatial superposition on the vector boundary data of the intertidal zone and the extraction result of the determination module, filtering the mask on the result of the determination module, removing the area which does not meet the growth requirement of spartina alterniflora, and obtaining the final spartina alterniflora extraction result.
5. A computer storage medium, wherein a computer program is stored in the computer storage medium; the computer program, when running on a computer, causes the computer to execute the method for constructing the alternanthera philoxeroides weathered index based on the Sentinel-2 optical image according to any one of claims 1 to 3.
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| CN202311216584.1A CN117409313B (en) | 2023-09-20 | 2023-09-20 | Sentinel-2 optical image-based method for constructing index of spartina alterniflora in weathering period |
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| WO2022166939A1 (en) * | 2021-02-08 | 2022-08-11 | 南京农业大学 | Method for improving estimation of leaf area index of early growth stage of wheat based on red-edge bands of sentinel-2 satellite images |
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