CN111693463B - Antarctic peninsula optimized lichen coverage index extraction method - Google Patents
Antarctic peninsula optimized lichen coverage index extraction method Download PDFInfo
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- CN111693463B CN111693463B CN202010522800.5A CN202010522800A CN111693463B CN 111693463 B CN111693463 B CN 111693463B CN 202010522800 A CN202010522800 A CN 202010522800A CN 111693463 B CN111693463 B CN 111693463B
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
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Abstract
The invention discloses an extraction method of an optimized lichen coverage index of a south-pole peninsula. Based on the spectral characteristics of the reflection region of the blue wave band-near coast wave band, which is the characteristic reflection region of lichen different from other ground features, the lichen coverage index is designed and optimized to reflect the coverage and distribution of the front vegetation lichen in the Antarctic region. Due to the convenience of lichen coverage index calculation, compared with the traditional classification method, the method can quickly extract the lichen coverage and distribution information of a large area in the Antarctic region on the basis of the remote sensing image, construct and generate a high-resolution lichen index product, and can be used for monitoring the large-range dynamic change of the Antarctic vegetation lichen.
Description
Technical Field
The invention relates to the technical field of remote sensing, in particular to an extraction method for an optimized lichen coverage index of a south-pole peninsula.
Background
Antarctic vegetation is sensitive to changes in the natural environment and ecosystem and is an indicator of climate change. Lichens exist widely in the south pole environment, but the current methods for accurately extracting lichens distribution are mostly limited to a hard classification method, a spectral unmixing method and a normalized vegetation index NDVI detection method. However, the hard classification method can achieve a more accurate result by combining results of a plurality of classifiers, the spectral unmixing method is time-consuming and inefficient, and both methods cannot rapidly obtain a large range of moss distribution. Traditional vegetation indices NDVI tend to underestimate the presence of lichen, making NDVI extract lichen coverage limited. There is currently no index reflecting moss coverage and no wide range of high resolution vegetation moss index products in the south peninsula area to indicate moss distribution.
Disclosure of Invention
In order to solve the problem, an optimized lichen coverage index is designed based on the reflection spectrum characteristics, and the coverage and distribution conditions of the lichen of the pioneer vegetation in the Antarctic region are reflected.
The invention discloses a method for extracting an optimized lichen coverage index of a south Pole peninsula, which comprises the following steps: acquiring cloud-free and high-quality remote sensing image data capable of covering a south-pole peninsula area; carrying out image preprocessing to obtain the blue waveband reflectivity of the remote sensing data and the offshore shore waveband reflectivity of the remote sensing data; resampling the near-coast wave band resolution to be consistent with the blue wave band resolution; based on the spectral characteristics of the reflection region of the blue wave band-near coast wave band, which is a characteristic reflection region of lichen different from other ground features, an optimized lichen coverage index for reflecting the coverage and distribution of the pioneer vegetation lichen in the south pole region is designed, and is expressed as:
wherein Blue is the Blue band reflectivity of the remote sensing data; coastal is the reflectivity of the offshore shore wave band of the remote sensing data; the value 0.16 is a regulating parameter for reducing the influence of background rocks or soil on lichen; sequentially calculating each image to generate an optimized lichen coverage index product; and splicing and cutting all the images, and processing abnormal values to finally generate the Antarctic peninsula optimized lichen coverage index product.
In the method for extracting the optimized lichen coverage index of the Antarctic peninsula, the remote sensing image is preferably a Worldview-2 or Sentinel-2 satellite sensor image.
In the method for extracting the south pole peninsula optimized lichen coverage index, preferably, the image preprocessing refers to radiometric calibration and atmospheric correction of a remote sensing image.
In the method for extracting the south pole peninsula optimized lichen coverage index, preferably, the central wavelength of the blue band is about 420 nm; the offshore shore band center wavelength is about 480 nm.
In the method for extracting the Antarctic peninsula moss coverage index, the resolution is preferably 10m.
In the method for extracting the optimized lichen coverage index of the Antarctic peninsula, preferably, the abnormal value processing means that the range of the optimized lichen coverage index is (-1,1), and the value outside the range is regarded as an abnormal value to be removed or corrected.
Due to the fact that convenience of lichen coverage index calculation is optimized, compared with a traditional classification method, the method can quickly extract the large-area lichen coverage and distribution information of the Antarctic region on the basis of remote sensing images, and can construct and generate a high-resolution lichen index product for monitoring the large-range dynamic change of the lichen of Antarctic vegetation.
Drawings
Fig. 1 is a flow chart of the method for extracting the optimized lichen coverage index of the Antarctic peninsula.
FIG. 2A is a measured spectrum of the lichen spectrum for four sample areas.
FIG. 2B is the end-member spectrum of the lichen spectrum for the four sample areas.
FIG. 3A is a graph of correlation between measured lichen coverage sample data and OLCI values
FIG. 3B is a verification of the correlation of stochastic point model simulation data to OLCI values.
Fig. 4 is an OLCI vegetation index product for high coverage area of pioneer vegetation in south-pole peninsula.
Fig. 5A is a plot of OLCI vegetation index product for feldspathic peninsula.
Figure 5B is a partial view of the OLCI vegetation index product from james ross island.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly and completely understood, the technical solutions in the embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention, and it should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention. The described embodiments are only some embodiments of the invention, not all embodiments. 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.
FIG. 1 is a flow chart of the method for extracting the coverage index of Antarctic peninsula moss of the present invention. The design of the Optimized Lichen Coverage Index (OLCI) is based on the spectral reflection characteristics of Lichen and the traditional vegetation Index OSAVI design form.
As shown in fig. 2, the valleys and reflective shoulders of the "U-shaped" reflection of lichen can be captured by the blue band and the coastal shore band of the remote sensing data, and the spectral characteristics of the reflection region of the blue band-coastal band are different from the characteristic reflection regions of other ground features, so that the OLCI is designed to reflect the coverage and distribution of the front vegetation lichen in the south pole area. OLCI can be used for satellite sensors such as Worldview-2, sentinel-2 and the like to generate lichen index products in Antarctic regions.
Taking the Sentinel-2 data processing as an example, the south-pole peninsula Optimized Lichen Coverage Index (OLCI) extraction procedure is as follows:
in step S1, sentinel-2 image data with high quality and no cloud in the area covering the south peninsula is selected, and the image time is between 1 month and 3 months because lichen appears in summer.
In step S2, image preprocessing is performed, and radiometric calibration and atmospheric correction are performed on the remote sensing image through a Sen2Cor plug-in unit, so as to obtain an apparent accurate reflectivity of the ground object.
In step S3, because the resolution of the spectral band of Sentinel-2 is not uniform, the resolution of the near-coast band is resampled to 10m by the Sen2Res plug-in, and the resolution is kept consistent with the blue band.
In step S4, the south pole peninsula optimized lichen coverage index is formulated as follows:
wherein Blue is the reflectivity of a Blue wave band of remote sensing data, and the central wavelength of the Blue wave band is about 420 nm; coastal is the reflectivity of the near-coast wave band of the remote sensing data, and the central wavelength is about 480 nm; the value 0.16 is a regulatory parameter for reducing the effect of background rocks or soil on lichen.
In step S5, each scene image is sequentially calculated by using an OLCI calculation formula, so as to generate an OLCI lichen index product.
In step S6, OLCI product post-processing is performed, all OLCI images are stitched and cropped, and abnormal value processing is performed. The OLCI range is (-1,1), therefore, the value outside the range is regarded as an abnormal value to be removed or corrected, and finally the high-resolution optimized lichen coverage index product of the south pole peninsula pioneer vegetation is generated.
To further illustrate the technical effects of the present invention, the following description is made with reference to the accompanying drawings. FIG. 3A shows a correlation validation of measured lichen coverage sample data with OLCI values, both R 2 Is 0.7236. FIG. 3B illustrates the correlation verification of random point model simulation data with OLCI values. Generating 1280 random points, and performing correlation analysis on the model simulated lichen coverage data and the OLCI numerical value, wherein R is the number of the random points 2 Is 0.7147. Thus, OLCI has a good correlation with lichen coverage, demonstrating that OLCI indicates the effectiveness of lichen coverage.
Fig. 4, 5A and 5B are high coverage area or detail views of OLCI lichen index product. The graph shows that the Sentinel-2 optimized vegetation index product can effectively and quickly extract the distribution of the pioneer vegetation, and lays a foundation for subsequently monitoring the response input of the dynamic change and the climate change of the pioneer vegetation lichens.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (6)
1. A method for extracting an optimized lichen coverage index of a south-pole peninsula is characterized in that,
the method comprises the following steps:
acquiring cloud-free and high-quality remote sensing image data capable of covering a south-pole peninsula area;
carrying out image preprocessing to obtain the blue waveband reflectivity of the remote sensing data and the offshore shore waveband reflectivity of the remote sensing data;
resampling the near-coast wave band resolution to be consistent with the blue wave band resolution;
based on the spectral characteristics of the reflection region of the blue wave band-near coast wave band, which is a characteristic reflection region of lichens different from other ground objects, an optimized lichen coverage index for reflecting the coverage and distribution of the lichens of the pioneer vegetation in the south pole area is designed, and is expressed as follows:
wherein Blue is the Blue band reflectivity of the remote sensing data; coastal is the reflectivity of the offshore shore wave band of the remote sensing data; the value 0.16 is a regulating parameter for reducing the influence of background rocks or soil on lichen;
sequentially calculating each image to generate an optimized lichen coverage index product;
and splicing and cutting all the images, and processing abnormal values to finally generate the Antarctic peninsula optimized lichen coverage index product.
2. The south Pole peninsula moss coverage index extraction method as claimed in claim 1, wherein,
the remote sensing image is a Worldview-2 or Sentinel-2 satellite sensor image.
3. The south Pole peninsula moss coverage index extraction method as claimed in claim 1, wherein,
the image preprocessing refers to radiometric calibration and atmospheric correction of the remote sensing image.
4. The south Pole peninsula moss coverage index extraction method as claimed in claim 1, wherein,
the central wavelength of the blue band is about 420 nm; the offshore shore band center wavelength is about 480 nm.
5. The south Pole peninsula moss coverage index extraction method as claimed in claim 1, wherein,
the resolution is 10m.
6. The south Pole peninsula moss coverage index extraction method as claimed in claim 1, wherein,
the abnormal value processing means that the range of the index of the optimized lichen coverage degree is (-1,1), and the value outside the range is regarded as an abnormal value to be removed or corrected.
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| CN103439297A (en) * | 2013-09-05 | 2013-12-11 | 太原理工大学 | Remote sensing estimation method for fresh weights of green plants in desert grassland |
| CN108537795A (en) * | 2018-04-23 | 2018-09-14 | 中国科学院地球化学研究所 | A kind of mountain stream information extracting method |
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| CN103439297A (en) * | 2013-09-05 | 2013-12-11 | 太原理工大学 | Remote sensing estimation method for fresh weights of green plants in desert grassland |
| CN108537795A (en) * | 2018-04-23 | 2018-09-14 | 中国科学院地球化学研究所 | A kind of mountain stream information extracting method |
| CN109211798A (en) * | 2018-11-16 | 2019-01-15 | 福州大学 | A kind of annual sea ice distributed intelligence extracting method based on remote sensing image spectral signature |
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